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An electrochemical sensor based on an electroactive nanocomposite was designed for the first time consisting of electrochemically reduced graphene oxide (ERGO), polyaniline (PANI), and poly(alizarin red S) (PARS) for ciprofloxacin (CIPF) detection. The ERGO/PANI/PARS-modified screen-printed carbon electrode (SPCE) was constructed through a three-step electrochemical protocol and characterized using FTIR, UV-visible spectroscopy, FESEM, CV, LSV, and EIS. The new electrochemical CIPF sensor demonstrated a low detection limit of 0.0021 µM, a broad linear range of 0.01 to 69.8 µM, a high sensitivity of 5.09 µA/µM/cm2, and reasonable selectivity and reproducibility. Moreover, the ERGO/PANI/PARS/SPCE was successfully utilized to determine CIPF in milk with good recoveries and relative standard deviation (< 5%), which were close to those with HPLC analysis.
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Compostos de Anilina , Antraquinonas , Carbono , Ciprofloxacina , Técnicas Eletroquímicas , Eletrodos , Grafite , Limite de Detecção , Leite , Grafite/química , Leite/química , Compostos de Anilina/química , Técnicas Eletroquímicas/métodos , Técnicas Eletroquímicas/instrumentação , Animais , Ciprofloxacina/análise , Carbono/química , Antraquinonas/química , Reprodutibilidade dos Testes , Contaminação de Alimentos/análise , Antibacterianos/análiseRESUMO
Alcoholic liver disease (ALD) significantly affects immune cell function and leads to immunological dysregulation. This study explored the potential of granulocyte colony-stimulating factor (G-CSF) to mitigate the negative effects of alcohol on immune cells in a mouse model of ALD. To investigate the capacity of G-CSF, ALD was induced using a 17-day alcohol-enriched diet, followed by a single G-CSF dose prior to sampling. We focused on the dynamics of peripheral blood mononuclear cells using high-dimensional mass cytometry to detect subtle changes. Alcohol intake reduced the number of B cells, monocytes, dendritic cells, and NK cells while increasing the number of T cells. Notably, G-CSF treatment reversed the alcohol-induced increase in total CD4+ and CD8+ T cell populations. This effect was remarkable in naïve, effector CD4+ T cells and naïve CD8+ T cells. PhenoGraph and FlowSOM analysis further revealed the recovery effect of G-CSF on specific T cell subgroups, including central memory CD8+ T cells and double-negative T cells expressing Ly6chighCD44high, which are adversely affected by alcohol. These results enhance our understanding of the effect of ALD on immune function and suggest that G-CSF is a potential therapeutic agent, laying the foundation for future clinical research.
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Fator Estimulador de Colônias de Granulócitos , Hepatopatias Alcoólicas , Camundongos Endogâmicos C57BL , Animais , Fator Estimulador de Colônias de Granulócitos/farmacologia , Hepatopatias Alcoólicas/imunologia , Hepatopatias Alcoólicas/tratamento farmacológico , Hepatopatias Alcoólicas/patologia , Camundongos , Masculino , Linfócitos T CD8-Positivos/efeitos dos fármacos , Linfócitos T CD8-Positivos/imunologia , Modelos Animais de Doenças , Subpopulações de Linfócitos T/efeitos dos fármacos , Subpopulações de Linfócitos T/imunologia , Linfócitos T CD4-Positivos/efeitos dos fármacos , Linfócitos T CD4-Positivos/imunologia , Etanol/farmacologiaRESUMO
BACKGROUND: Age-dependent immune responses to coronavirus disease 2019 (COVID-19) vaccinations and breakthrough infections (BIs) in young and middle-aged individuals are unclear. METHODS: This nationwide multicenter prospective cohort study analyzed immune responses in participants of the ChAdOx1 (ChAd)-ChAd-mRNA vaccine group using cytometry by time-of-flight, anti-spike protein antibody (Sab) and anti-nucleocapsid antibody (Nab) titers, plaque reduction neutralization tests (PRNTs), and interferon-gamma (IFN-γ) release assays at various time points. RESULTS: We evaluated 347 participants with an average age of 38.9 ± 9.4 years (range: 21-63). There was a significant inverse correlation between age and Sab levels after the second dose (slope - 14.96, P = 0.032), and this was more pronounced after the third dose (slope - 208.9, P < 0.001). After BIs, older participants showed significantly higher Sab titers (slope 398.8, P = 0.001), reversing the age-related decline observed post-vaccination. This reversal was also observed in PRNTs against wild-type SARS-CoV-2 and the BA.1 and BA.5 variants. IFN-γ responses increased markedly after the third dose and Bis, but showed a weak positive correlation with age, without statistical significance. Immune cell profiling revealed an age-dependent decrease in the proportions of B-cell lineage cells. The proportions of naive CD4+ and CD8+ T cells were inversely correlated with age, whereas the proportions of mature T cell subsets with memory function, including memory CD4+ T, CD8+ TEM, CD8+ TEMRA, and TFH cells, increased with age. CONCLUSIONS: Age-dependent waning of the serologic response to COVID-19 vaccines occurred even in middle-aged individuals, but was reversed after BIs. IFN-γ responses were preserved, compensating for the decrease in naive T cell populations, with an increase in memory T cell populations.
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Understanding the sepsis-induced immunological response can be facilitated by identifying phenotypic changes in immune cells at the single-cell level. Mass cytometry, a novel multiparametric single-cell analysis technique, offers considerable benefits in characterizing sepsis-induced phenotypic changes in peripheral blood mononuclear cells. Here, we analyzed peripheral blood mononuclear cells from 20 sepsis patients and 10 healthy donors using mass cytometry and employing 23 markers. Both manual gating and automated clustering approaches (PhenoGraph) were used for cell identification, complemented by uniform manifold approximation and projection (UMAP) for dimensionality reduction and visualization. Our study revealed that patients with sepsis exhibited a unique immune cell profile, marked by an increased presence of monocytes, B cells, and dendritic cells, alongside a reduction in natural killer (NK) cells and CD4/CD8 T cells. Notably, significant changes in the distributions of monocytes and B and CD4 T cells were observed. Clustering with PhenoGraph unveiled the subsets of each cell type and identified elevated CCR6 expression in sepsis patients' monocyte subset (PG#5), while further PhenoGraph clustering on manually gated T and B cells discovered sepsis-specific CD4 T cell subsets (CCR4low CD20low CD38low) and B cell subsets (HLA-DRlow CCR7low CCR6high), which could potentially serve as novel diagnostic markers for sepsis.
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Citometria de Fluxo , Sepse , Humanos , Sepse/imunologia , Masculino , Feminino , Pessoa de Meia-Idade , Citometria de Fluxo/métodos , Monócitos/imunologia , Idoso , Leucócitos Mononucleares/imunologia , Fenótipo , Células Dendríticas/imunologia , Linfócitos B/imunologia , Adulto , Imunofenotipagem , Células Matadoras Naturais/imunologia , Linfócitos T CD4-Positivos/imunologia , Biomarcadores/sangue , Biomarcadores/análiseRESUMO
The assessment of AgNPs toxicity in vitro and in vivo models are frequently conflicting and inaccurate. Nevertheless, single cell immunological responses in a heterogenous environment have received little attention. Therefore, in this study, we have performed in-depth analysis which clearly revealed cellular-metal ion association as well as specific immunological response. Our study didn't show significant population differences in PMBC between control and AgNPs group implying no toxicological response. To confirm it further, deep profiling identified differences in subsets and differentially expressed genes (DEGs) of monocytes, B cells and T cells. Notably, monocyte subsets showed significant upregulation of metallothionein (MT) gene expression such as MT1G, MT1X, MT1E, MT1A, and MT1F. On the other hand, downregulation of pro-inflammatory genes such as IL1ß and CCL3 in both CD16 + and CD16- monocyte subsets were observed. This result indicated that AgNPs association with monocyte subsets de-promoted inflammatory responsive genes suggesting no significant toxicity observed in AgNPs treated group. Other cell types such as B cells and T cells also showed negligible differences in their subsets suggesting no toxicity response. Further, AgNPs treated group showed upregulation of cell proliferation, ribosomal synthesis, downregulation of cytokine release, and T cell differentiation inhibition. Overall, our results conclude that treatment of AgNPs to PMBC cells didn't display immunological related cytotoxicity response and thus motivate researchers to use them actively for biomedical applications.
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Nanopartículas Metálicas , Prata , Prata/farmacologia , Análise da Expressão Gênica de Célula Única , Metalotioneína/genética , Monócitos/metabolismoRESUMO
Computational modeling has earned significant interest as an alternative to animal testing of toxicity assessment. However, the process of selecting an appropriate algorithm and fine-tuning hyperparameters for the developing of optimized models takes considerable time, expertise, and an intensive search. The recent emergence of automated machine learning (autoML) approaches, available as user-friendly platforms, has proven beneficial for individuals with limited knowledge in ML-based predictive model development. These autoML platforms automate crucial steps in model development, including data preprocessing, algorithm selection, and hyperparameter tuning. In this study, we used seven previously published and publicly available datasets for oxides and metals to develop nanotoxicity prediction models. AutoML platforms, namely Vertex AI, Azure, and Dataiku, were employed and performance measures such as accuracy, F1 score, precision, and recall for these autoML-based models were then compared with those of conventional ML-based models. The results demonstrated clearly that the autoML platforms produced more reliable nanotoxicity prediction models, outperforming those built with conventional ML algorithms. While none of the three autoML platforms significantly outperformed the others, distinctions exist among them in terms of the available options for choosing technical features throughout the model development steps. This allows users to select an autoML platform that aligns with their knowledge of predictive model development and its technical features. Additionally, prediction models constructed from datasets with better data quality displayed, enhanced performance than those built from datasets with lower data quality, indicating that future studies with high-quality datasets can further improve the performance of those autoML-based prediction models.
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To gain a better understanding of the complex human immune system, it is necessary to measure and interpret numerous cellular protein expressions at the single cell level. Mass cytometry is a relatively new technology that offers unprecedented information about the protein expression of a single cell. Conversely, the analysis of high-dimensional and multiparametric mass cytometric data sets presents a new computational challenge. For instance, conventional "manual gating" analysis was inefficient and unreliable for multiparametric phenotyping of the heterogeneous immune cellular system; consequently, automated methods have been developed to address the high dimensionality of mass cytometry data and enhance the reproducibility of the analysis. Here, we present CyGate, a semiautomated method for classifying single cells into their respective cell types. CyGate learns a gating strategy from a reference data set, trains a model for cell classification, and then automatically analyzes additional data sets using the trained model. CyGate also supports the machine learning framework for the classification of "ungated" cells, which are typically disregarded by automated methods. CyGate's utility was demonstrated by its high performance in cell type classification and the lowest generalization error on various public data sets when compared to the state-of-the-art semiautomated methods. Notably, CyGate had the shortest execution time, allowing it to scale with a growing number of samples. CyGate is available at https://github.com/seungjinna/cygate.
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Biologia Computacional , Aprendizado de Máquina , Humanos , Citometria de Fluxo/métodos , Reprodutibilidade dos Testes , Biologia Computacional/métodos , AlgoritmosRESUMO
This paper proposes an intelligent framework for the fault diagnosis of centrifugal pumps (CPs) based on wavelet coherence analysis (WCA) and deep learning (DL). The fault-related impulses in the CP vibration signal are often attenuated due to the background interference noises, thus affecting the sensitivity of the traditional statistical features towards faults. Furthermore, extracting health-sensitive information from the vibration signal needs human expertise and background knowledge. To extract CP health-sensitive features autonomously from the vibration signals, the proposed approach initially selects a healthy baseline signal. The wavelet coherence analysis is then computed between the healthy baseline signal and the signal obtained from a CP under different operating conditions, yielding coherograms. WCA is a signal processing technique that is used to measure the degree of linear correlation between two signals as a function of frequency. The coherograms carry information about the CP vulnerability towards the faults as the color intensity in the coherograms changes according to the change in CP health conditions. To utilize the changes in the coherograms due to the health conditions of the CP, they are provided to a Convolution Neural Network (CNN) and a Convolution Autoencoder (CAE) for the extraction of discriminant CP health-sensitive information autonomously. The CAE extracts global variations from the coherograms, and the CNN extracts local variations related to CP health. This information is combined into a single latent space vector. To identify the health conditions of the CP, the latent space vector is classified using an Artificial Neural Network (ANN). The proposed method identifies faults in the CP with higher accuracy as compared to already existing methods when it is tested on the vibration signals acquired from real-world industrial CPs.
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Understanding how nanoparticles (NPs) interact with biological systems is important in many biomedical research areas. However, the heterogeneous nature of biological systems, including the existence of numerous cell types and multitudes of key environmental factors, makes these interactions extremely challenging to investigate precisely. Here, using a single-cell-based, high-dimensional mass cytometry approach, we demonstrated that the presence of protein corona has significant influences on the cellular associations and cytotoxicity of gold NPs for human immune cells, and those effects vary significantly with the types of immune cells and their subsets. The altered surface functionality of protein corona reduced the cytotoxicity and cellular association of gold NPs in most cell types (e.g., monocytes, dendritic cells, B cells, natural killer (NK) cells, and T cells) and those immune cells selected different endocytosis pathways such as receptor-mediated endocytosis, phagocytosis, and micropinocytosis. However, even slight alterations in the major cell type (phagocytic cells and non-phagocytic cells) and T cell subsets (e.g., memory and naive T cells) resulted in significant protein corona-dependent variations in their cellular dose of gold NPs. Especially, naive T killer cells exhibited additional heterogeneity than memory T killer cells, with clusters exhibiting distinct cellular association patterns in single-cell contour plots. This multi-parametric analysis of mass cytometry data established a conceptual framework for a more holistic understanding of how the human immune system responds to external stimuli, paving the way for the application of precisely engineered NPs as promising tools of nanomedicine under various clinical settings, including targeted drug delivery and vaccine development.
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Among 17 Panax species identified across the world, Panax ginseng (Korean ginseng), Panax quinquefolius (American ginseng), and Panax notoginseng (Chinese ginseng) are highly recognized for the presence of bioactive compound, ginsenosides and their pharmacological effects. P. ginseng is widely used for synthesis of different types of nanoparticles compared to P. quinquefolius and P. notoginseng. The use of nano-ginseng could increase the oral bioavailability, membrane permeability, and thus provide effective delivery of ginsenosides to the target sites through transport system. In this review, we explore the synthesis of ginseng nanoparticles using plant extracts from various organs, microbes, and polymers, as well as their biomedical applications. Furthermore, we highlight transporters involved in transport of ginsenoside nanoparticles to the target sites. Size, zeta potential, temperature, and pH are also discussed as the critical parameters affecting the quality of ginseng nanoparticles synthesis.
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Ginsenosídeos , Panax , Humanos , Ginsenosídeos/farmacologia , Panax/química , Extratos Vegetais/químicaRESUMO
Heterosynaptic neuromodulation is a key enabler for energy-efficient and high-level biological neural processing. However, such manifold synaptic modulation cannot be emulated using conventional memristors and synaptic transistors. Thus, reported herein is a three-terminal heterosynaptic memtransistor using an intentional-defect-generated molybdenum disulfide channel. Particularly, the defect-mediated space-charge-limited conduction in the ultrathin channel results in memristive switching characteristics between the source and drain terminals, which are further modulated using a gate terminal according to the gate-tuned filling of trap states. The device acts as an artificial synapse controlled by sub-femtojoule impulses from both the source and gate terminals, consuming lower energy than its biological counterpart. In particular, electrostatic gate modulation, corresponding to biological neuromodulation, additionally regulates the dynamic range and tuning rate of the synaptic weight, independent of the programming (source) impulses. Notably, this heterosynaptic modulation not only improves the learning accuracy and efficiency but also reduces energy consumption in the pattern recognition. Thus, the study presents a new route leading toward the realization of highly networked and energy-efficient neuromorphic electronics.
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Eletrônica , Molibdênio , Fenômenos Físicos , Eletricidade Estática , SinapsesRESUMO
In the machine learning and data science pipelines, feature extraction is considered the most crucial component according to researchers, where generating a discriminative feature matrix is the utmost challenging task to achieve high classification accuracy. Generally, the classical feature extraction techniques are sensitive to the noisy component of the signal and need more time for training. To deal with these issues, a comparatively new feature extraction technique, referred to as a wavelet scattering transform (WST) is utilized, and incorporated with ML classifiers to design a framework for bearing fault classification in this paper. The WST is a knowledge-based technique, and the structure is similar to the convolution neural network. This technique provides low-variance features of real-valued signals, which are usually necessary for classification tasks. These signals are resistant to signal deformation and preserve information at high frequencies. The current signal data from a publicly available dataset for three different bearing conditions are considered. By combining the scattering path coefficients, the decomposition coefficients from the 0th and 1st layers are considered as features. The experimental results demonstrate that WST-based features, when used with ensemble ML algorithms, could achieve more than 99% classification accuracy. The performance of ANN models with these features is similar. This work exhibits that utilizing WST coefficients for the motor current signal as features can improve the bearing fault classification accuracy when compared to other feature extraction approaches such as empirical wavelet transform (EWT), information fusion (IF), and wavelet packet decomposition (WPD). Thus, our proposed approach can be considered as an effective classification method for the fault diagnosis of rotating machinery.
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Redes Neurais de Computação , Análise de Ondaletas , Aprendizado de Máquina , AlgoritmosRESUMO
In recent decades, upconversion nanomaterials (UCNMs) have attracted considerable research interest because of their unique optical properties, such as large anti-Stokes shifts, sharp emissions, non-photobleaching, and long lifetime. These unique properties make them ideal candidates for unified applications in biomedical fields, including drug delivery, bioimaging, biosensing, and photodynamic therapy for specific cancers. This review describes the general mechanisms of upconversion, synthesis methods, and potential applications in biology and their biological responses. Additionally, the biological toxicity of UCNMs is explained and summarized with the associated intracellular association mechanisms. Finally, the prospects and future challenges of UCNMs at the clinical level in biological applications are described, along with a summary of opportunity for biological as well as clinical applications of UCNMs.
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Due to miscommunication a number of potential co-authors were not listed in the original publication [...].
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Diagnostics of mechanical problems in manufacturing systems are essential to maintaining safety and minimizing expenditures. In this study, an intelligent fault classification model that combines a signal-to-image encoding technique and a convolution neural network (CNN) with the motor-current signal is proposed to classify bearing faults. In the beginning, we split the dataset into four parts, considering the operating conditions. Then, the original signal is segmented into multiple samples, and we apply the Gramian angular field (GAF) algorithm on each sample to generate two-dimensional (2-D) images, which also converts the time-series signals into polar coordinates. The image conversion technique eliminates the requirement of manual feature extraction and creates a distinct pattern for individual fault signatures. Finally, the resultant image dataset is used to design and train a 2-layer deep CNN model that can extract high-level features from multiple images to classify fault conditions. For all the experiments that were conducted on different operating conditions, the proposed method shows a high classification accuracy of more than 99% and proves that the GAF can efficiently preserve the fault characteristics from the current signal. Three built-in CNN structures were also applied to classify the images, but the simple structure of a 2-layer CNN proved to be sufficient in terms of classification results and computational time. Finally, we compare the experimental results from the proposed diagnostic framework with some state-of-the-art diagnostic techniques and previously published works to validate its superiority under inconsistent working conditions. The results verify that the proposed method based on motor-current signal analysis is a good approach for bearing fault classification in terms of classification accuracy and other evaluation parameters.
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Algoritmos , Redes Neurais de ComputaçãoRESUMO
Origanum vulgare essential oil (EO) is traditionally well-known for its aromatic properties and biomedical applications, including anticancer. This was the first report where oregano essential oil-based nano emulsion (OENE) was synthesized for studying its effects on prostate cancer cell lines (PC3). At first, we have synthesized OENE and characterized using various spectroscopic analyses. The toxicity and inhibitory concentration (IC50) of OENE toward prostate cancer by MTT analysis were performed. The lipid biogenesis mediated, molecular target pathway analyses were performed using fluorescence cellular staining techniques, real-time RT-PCR, or western blotting analysis. OENE showed IC50 at 13.82 µg/mL and significantly induced distinct morphological changes, including cell shrinkage, cell density, and cell shape reduction. In addition, OENE could also significantly decreased lipid droplet accumulation which was confirmed by studying mRNA transcripts of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR) (0.31-fold), fatty acid synthase (FASN) (0.18-fold), and sterol regulatory element-binding protein (SREPB1) (0.11-fold), respectively. Furthermore, there is a significant upregulation BAX (BCL2 associated X) and caspase 3 expressions. Nevertheless, OENE decreased the transcript level of BCL2 (B-cell lymphoma 2), thus resulting in apoptosis. Overall, our present work demonstrated that OENE could be a therapeutic target for the treatment of prostate cancer and warrants in vivo studies.
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During emission, TiO2 nanoparticles (NPs) might meet various chemicals, including metal ions and organic compounds in aquatic environments (e.g., surface water, sediments). At environmentally safe concentrations, combinations of both TiO2 NPs and those chemicals might cause cocktail effects (i.e., mixture toxicity) to aquatic organisms. Previous models such as concentration addition and independent action require dose-response curves of single components in the mixtures to predict the mixture toxicity. Structure-activity relationship (QSAR) models might predict the toxicity of nano-mixtures without dose-response curves of single components in the mixtures. However, current quantitative structure-activity relationship (QSAR) models are mainly focused on predicting cytotoxicity (i.e., cell viability) of heterogeneous metallic TiO2 nanoparticles (NPs) or mixtures of TiO2 NPs and four metal ions (Cu2+, Cd2+, Ni2+, and Zn2+). To minimize the experimental cost of nano-mixture risk assessment, in this study, we developed novel nano-mixture QSAR models to predict i) EC50 of 76 nano-mixtures containing TiO2 NPs and one of eight inorganic/organic compounds (i.e., AgNO3, Cd(NO3)2, Cu(NO3)2, CuSO4, Na2HAsO4, NaAsO2, Benzylparaben and Benzophenone-3), to Daphnia magna(D. magna), and ii) immobilization of D. magna exposed to one of 98 mixtures containing TiO2 NPs and one of eleven inorganic/organic compounds (i.e., AgNO3, Cd(NO3)2, Cu(NO3)2, CuSO4, Na2HAsO4, NaAsO2, Benzylparaben Benzophenone-3, Pirimicarb, Pentabromodiphenyl Ether and Triton X-100). The nano-mixture QSAR models were developed with mixture descriptors (Dmix) combing quantum descriptors of mixture components (e.g., TiO2 NPs and its partners) by using different machine learning techniques (i.e., random forest, neural network, support vector machine, and multiple linear regression). Nano-mixture QSAR models built with the random forest algorithm and proposed mixture descriptors exhibited good performance for predicting logEC50 (Adj.R2test = 0.955 ± 0.003, RMSEtest = 0.016 ± 0.002, and MAEtest = 0.008 ± 0.001) and immobilization (Adj.R2test = 0.888 ± 0.011, RMSEtest = 11.327 ± 0.730, and MAEtest = 5.933 ± 0.442). The models developed in this study were implemented in a user-friendly application for assessing the aquatic toxicity of TiO2 based nano-mixtures.
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Daphnia , Poluentes Químicos da Água , Animais , Cádmio/farmacologia , Compostos Orgânicos/farmacologia , Relação Quantitativa Estrutura-Atividade , Titânio , Poluentes Químicos da Água/toxicidadeRESUMO
Increasing production and application of silver nanoparticles (Ag NPs) have raised concerns on their possible adverse effects on human health. However, a comprehensive understanding of their effects on biological systems, especially immunomodulatory responses involving various immune cell types and biomolecules (e.g., cytokines and chemokines), is still incomplete. In this study, a single-cell-based, high-dimensional mass cytometry approach is used to investigate the immunomodulatory responses of Ag NPs using human peripheral blood mononuclear cells (hPBMCs) exposed to poly-vinyl-pyrrolidone (PVP)-coated Ag NPs of different core sizes (i.e., 10-, 20-, and 40-nm). Although there were no severe cytotoxic effects observed, PVPAg10 and PVPAg20 were excessively found in monocytes and dendritic cells, while PVPAg40 displayed more affinity with B cells and natural killer cells, thereby triggering the release of proinflammatory cytokines such as IL-2, IL-17A, IL-17F, MIP1ß, TNFα, and IFNγ. Our findings indicate that under the exposure conditions tested in this study, Ag NPs only triggered the inflammatory responses in a size-dependent manner rather than induce cytotoxicity in hPBMCs. Our study provides an appropriate ex vivo model to better understand the human immune responses against Ag NP at a single-cell level, which can contribute to the development of targeted drug delivery, vaccine developments, and cancer radiotherapy treatments.
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The number of patients with liver diseases has increased significantly with the progress of global industrialization. Hepatic fibrosis, one of the most common liver diseases diagnosed in many developed countries, occurs in response to chronic liver injury and is primarily driven by the development of inflammation. Earlier immunological studies have been focused on the importance of the innate immune response in the pathophysiology of steatohepatitis and fibrosis, but recently, it has also been reported that adaptive immunity, particularly B cells, plays an essential role in hepatic inflammation and fibrosis. However, despite recent data showing the importance of adaptive immunity, relatively little is known about the role of B cells in the pathogenesis of steatohepatitis fibrosis. In this study, a single-cell-based, high-dimensional mass cytometric investigation of the peripheral blood mononuclear cells collected from mice belonging to three groups [normal chow (NC), thioacetamide (TAA), and 11beta-HSD inhibitor drug] was conducted to further understand the pathogenesis of liver fibrosis through reliable noninvasive biomarkers. Firstly, major immune cell types and their population changes were qualitatively analyzed using UMAP dimensionality reduction and two-dimensional visualization technique combined with a conventional manual gating strategy. The population of B cells displayed a twofold increase in the TAA group compared to that in the NC group, which was recovered slightly after treatment with the 11beta-HSD inhibitor drug. In contrast, the populations of NK cells, effector CD4+ T cells, and memory CD8+ T cells were significantly reduced in the TAA group compared with those in the NC group. Further identification and quantification of the major immune cell types and their subsets were conducted based on automated clustering approaches [PhenoGraph (PG) and FlowSOM]. The B-cell subset corresponding to PhenoGraph cluster PG#2 (CD62LhighCD44highLy6chigh B cells) and PG#3 (CD62LhighCD44highLy6clow B cell) appears to play a major role in both the development of hepatic fibrosis and recovery via treatment, whereas PG#1 (CD62LlowCD44highLy6clow B cell) seems to play a dominant role in the development of hepatic fibrosis. These findings provide insights into the roles of cellular subsets of B cells during the progression of, and recovery from, hepatic fibrosis.
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Linfócitos T CD8-Positivos , Leucócitos Mononucleares , Animais , Linfócitos T CD8-Positivos/metabolismo , Humanos , Inflamação/patologia , Leucócitos Mononucleares/metabolismo , Cirrose Hepática/patologia , Camundongos , TioacetamidaRESUMO
Whereas the characterization of nanomaterials using different analytical techniques is often highly automated and standardized, the sample preparation that precedes it causes a bottleneck in nanomaterial analysis as it is performed manually. Usually, this pretreatment depends on the skills and experience of the analysts. Furthermore, adequate reporting of the sample preparation is often missing. In this overview, some solutions for techniques widely used in nano-analytics to overcome this problem are discussed. Two examples of sample preparation optimization by automation are presented, which demonstrate that this approach is leading to increased analytical confidence. Our first example is motivated by the need to exclude human bias and focuses on the development of automation in sample introduction. To this end, a robotic system has been developed, which can prepare stable and homogeneous nanomaterial suspensions amenable to a variety of well-established analytical methods, such as dynamic light scattering (DLS), small-angle X-ray scattering (SAXS), field-flow fractionation (FFF) or single-particle inductively coupled mass spectrometry (sp-ICP-MS). Our second example addresses biological samples, such as cells exposed to nanomaterials, which are still challenging for reliable analysis. An air-liquid interface has been developed for the exposure of biological samples to nanomaterial-containing aerosols. The system exposes transmission electron microscopy (TEM) grids under reproducible conditions, whilst also allowing characterization of aerosol composition with mass spectrometry. Such an approach enables correlative measurements combining biological with physicochemical analysis. These case studies demonstrate that standardization and automation of sample preparation setups, combined with appropriate measurement processes and data reduction are crucial steps towards more reliable and reproducible data.