scRNA-seq analysis discovered suppression of immunomodulatory dependent inflammatory response in PMBCs exposed to silver nanoparticles.

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.

Overcoming the age-dependent SARS-CoV-2 vaccine response through hybrid immunity: analysis of humoral and cellular immunity with mass cytometry profiling.

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.

Disposable electrochemical sensors based on reduced graphene oxide/polyaniline/poly(alizarin red S)-modified integrated carbon electrodes for the detection of ciprofloxacin in milk.

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.

A comprehensive and systemic review of ginseng-based nanomaterials: Synthesis, targeted delivery, and biomedical applications.

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.

CyGate Provides a Robust Solution for Automatic Gating of Single Cell Cytometry Data.

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.

An Intelligent Framework for Fault Diagnosis of Centrifugal Pump Leveraging Wavelet Coherence Analysis and Deep Learning.

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.

Scanning transmission X-ray microscopy study of subcellular granules in human platelets at the carbon K- and calcium L2,3-edges.

Platelets and their subcellular components (e.g., dense granules) are essential components in hemostasis. Understanding their chemical heterogeneities at the sub-micrometer scale, particularly their activation during hemostasis and production of platelet-derived extracellular vesicles, may provide important insights into their mechanisms; however, this has rarely been investigated, mainly owing to the lack of appropriate chemical characterization tools at nanometer scale. Here, the use of scanning transmission X-ray microscopy (STXM) combined with X-ray absorption near edge structure (XANES) to characterize human platelets and their subcellular components at the carbon K-edge and calcium L2,3-edge, is reported. STXM images can identify not only the spatial distribution of subcellular components in human platelets, such as dense granules (DGs) with sizes of ~200 nm, but also their granule-to-granule chemical heterogeneities on the sub-micrometer scale, based on their XANES spectra. The calcium distribution map as well as the principal component analysis of the STXM image stacks clearly identified the numbers and locations of the calcium-rich DGs within human platelets. Deconvolution of the carbon K-edge XANES spectra, extracted from various locations in the platelets, showed that amide carbonyl and carboxylic acid functional groups were mainly found in the cytoplasm, while ketone-phenol-nitrile-imine, aliphatic, and carbonate functional groups were dominant in the platelet DGs. These observations suggest that platelet DGs are most likely composed of calcium polyphosphate associated with adenosine triphosphate (ATP) and adenosine diphosphate (ADP), with significant granule-to-granule variations in their compositions, while the cytoplasm regions of platelets contain significant amounts of proteins.

Classification Framework of the Bearing Faults of an Induction Motor Using Wavelet Scattering Transform-Based Features.

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.

A Bearing Fault Classification Framework Based on Image Encoding Techniques and a Convolutional Neural Network under Different Operating Conditions.

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.

Automation and Standardization-A Coupled Approach towards Reproducible Sample Preparation Protocols for Nanomaterial Analysis.

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.

Correction: Peters et al. Benchmarking the ACEnano Toolbox for Characterisation of Nanoparticle Size and Concentration by Interlaboratory Comparison. Molecules 2021, 26, 5315.

Due to miscommunication a number of potential co-authors were not listed in the original publication [...].

Benchmarking the ACEnano Toolbox for Characterisation of Nanoparticle Size and Concentration by Interlaboratory Comparisons.

ACEnano is an EU-funded project which aims at developing, optimising and validating methods for the detection and characterisation of nanomaterials (NMs) in increasingly complex matrices to improve confidence in the results and support their use in regulation. Within this project, several interlaboratory comparisons (ILCs) for the determination of particle size and concentration have been organised to benchmark existing analytical methods. In this paper the results of a number of these ILCs for the characterisation of NMs are presented and discussed. The results of the analyses of pristine well-defined particles such as 60 nm Au NMs in a simple aqueous suspension showed that laboratories are well capable of determining the sizes of these particles. The analysis of particles in complex matrices or formulations such as consumer products resulted in larger variations in particle sizes within technologies and clear differences in capability between techniques. Sunscreen lotion sample analysis by laboratories using spICP-MS and TEM/SEM identified and confirmed the TiO2 particles as being nanoscale and compliant with the EU definition of an NM for regulatory purposes. In a toothpaste sample orthogonal results by PTA, spICP-MS and TEM/SEM agreed and stated the TiO2 particles as not fitting the EU definition of an NM. In general, from the results of these ILCs we conclude that laboratories are well capable of determining particle sizes of NM, even in fairly complex formulations.

Mass Cytometry and Single-Cell RNA-seq Profiling of the Heterogeneity in Human Peripheral Blood Mononuclear Cells Interacting with Silver Nanoparticles.

Understanding the interactions between nanoparticles (NPs) and human immune cells is necessary for justifying their utilization in consumer products and biomedical applications. However, conventional assays may be insufficient in describing the complexity and heterogeneity of cell-NP interactions. Herein, mass cytometry and single-cell RNA-sequencing (scRNA-seq) are complementarily used to investigate the heterogeneous interactions between silver nanoparticles (AgNPs) and primary immune cells. Mass cytometry reveals the heterogeneous biodistribution of the positively charged polyethylenimine-coated AgNPs in various cell types and finds that monocytes and B cells have higher association with the AgNPs than other populations. scRNA-seq data of these two cell types demonstrate that each type has distinct responses to AgNP treatment: NRF2-mediated oxidative stress is confined to B cells, whereas monocytes show Fcγ-mediated phagocytosis. Besides the between-population heterogeneity, analysis of single-cell dose-response relationships further reveals within-population diversity for the B cells and naïve CD4+ T cells. Distinct subsets having different levels of cellular responses with respect to their cellular AgNP doses are found. This study demonstrates that the complementary use of mass cytometry and scRNA-seq is helpful for gaining in-depth knowledge on the heterogeneous interactions between immune cells and NPs and can be incorporated into future toxicity assessments of nanomaterials.

Identification of Ca-rich dense granules in human platelets using scanning transmission X-ray microscopy.

Whole-mount (WM) platelet preparation followed by transmission electron microscopy (TEM) observation is the standard method currently used to assess dense granule (DG) deficiency (DGD). However, due to the electron-density-based contrast mechanism in TEM, other granules such as α-granules might cause false DG detection. Here, scanning transmission X-ray microscopy (STXM) was used to identify DGs and minimize false DG detection of human platelets. STXM image stacks of human platelets were collected at the calcium (Ca) L2,3 absorption edge and then converted to optical density maps. Ca distribution maps, obtained by subtracting the optical density maps at the pre-edge region from those at the post-edge region, were used to identify DGs based on the Ca richness. DGs were successfully detected using this STXM method without false detection, based on Ca maps for four human platelets. Spectral analysis of granules in human platelets confirmed that DGs contain a richer Ca content than other granules. The Ca distribution maps facilitated more effective DG identification than TEM which might falsely detect DGs. Correct identification of DGs would be important to assess the status of platelets and DG-related diseases. Therefore, this STXM method is proposed as a promising approach for better DG identification and diagnosis, as a complementary tool to the current WM TEM approach.

Quasi-SMILES-Based Nano-Quantitative Structure-Activity Relationship Model to Predict the Cytotoxicity of Multiwalled Carbon Nanotubes to Human Lung Cells.

Quantitative structure-activity relationship (QSAR) models for nanomaterials (nano-QSAR) were developed to predict the cytotoxicity of 20 different types of multiwalled carbon nanotubes (MWCNTs) to human lung cells by using quasi-SMILES. The optimal descriptors, recorded as quasi-SMILES, were encoded to represent the physicochemical properties and experimental conditions for the MWCNTs from 276 data records collected from previously published studies. The quasi-SMILES used to build the optimal descriptors were (i) diameter, (ii) length, (iii) surface area, (iv) in vitro toxicity assay, (v) cell line, (vi) exposure time, and (vii) dose. The model calculations were performed by using the Monte Carlo method and computed with CORAL software ( www.insilico.eu/coral ). The quasi-SMILES-based nano-QSAR model provided satisfactory statistical results ( R2 for internal validation data sets: 0.60-0.80; R2pred for external validation data sets: 0.81-0.88). The model showed potential for use in the estimation of human lung cell viability after exposure to MWCNTs with the following properties: diameter, 12-74 nm; length, 0.19-20.25 µm; surface area, 11.3-380.0 m2/g; and dose, 0-200 ppm.

Fast and non-destructive Raman spectroscopic determination of multi-walled carbon nanotube (MWCNT) contents in MWCNT/polydimethylsiloxane composites.

A versatile Raman spectroscopic method to determine the contents of carbon nanotubes (CNTs) in CNT/polydimethylsiloxane (PDMS) composites is demonstrated, and important issues directly related to the accuracy of the measurement have been investigated. Initially, Raman microscopic mappings over an area of 6.0 × 6.0 mm2 were carried out on CNT/PDMS composites, which revealed the existence of the partial localization of CNTs on a microscopic scale. Therefore, a laser illumination scheme covering a large sample area of 28.3 mm2 was employed to acquire a sample spectrum representative of the whole CNT concentration. The peak area ratio between the CNT and PDMS peaks clearly varied with the CNT concentration, whereas the reproducibility of measurements was degraded for the composites containing more than 3.0 wt% CNTs because of the decreased Raman sampling volume arising from the absorption of laser radiation by the CNTs. The laser penetration depth was semi-quantitatively investigated by observing the spectra of thin-sliced samples collected by positioning a Teflon disk behind the sample, and Monte Carlo simulations were employed to examine the internal photon propagation as well as explain the experimental observation. In summary, the fundamental issues affecting the Raman measurement of the CNT containing polymer matrix have been clearly addressed, and the finding here will be a beneficial basis for successful Raman spectroscopic analysis of different CNT-containing composites.

Flow Cytometry-Based Quantification of Cellular Au Nanoparticles.

There has been a great deal of research regarding the cellular association of nanoparticles (NPs), although there are only a few methods available yet for the quantitative measurements of cellular NPs. In this study, we propose a simple and quantitative method to estimate the cellular uptake of Au NPs into cervical cancer cells (HeLa) based on their side scattering (SSC) intensities measured by flow cytometry (FCM). We have compared SSC intensities of HeLa cells exposed to eight different types of Au NPs (40-100 nm size, with positive or negative surface charge) with the amount of cellular Au NPs measured by inductively coupled plasma mass spectrometry (ICPMS). On the basis of these comparisons, we have found linear correlations between the cellular Au NPs and the SSC intensities and used them to estimate the amount of Au NPs associated with HeLa cells. Once the correlations were found for specific cell lines and types of nanoparticles, this approach is useful for simple and quantitative estimation of the cellular Au NPs, without performing labor-intensive and complicated sample preparation procedures required for the ICPMS approach.

Influence of middle ear mucosal condition on post-tympanoplasty audiologic outcome.

In this study, the association between the middle ear mucosal condition and post-tympanoplasty audiologic outcome was investigated in patients with chronic otitis media without cholesteatoma. One hundred and forty-three patients with chronic otitis media were collected in the Department of Otorhinolaryngology-Head and Neck Surgery at Asan Medical Center between January, 2009 and December, 2011. In the course of tympanoplasty, the status of the middle ear mucosa was divided into normal or abnormal by one surgeon. Pure tone audiometry was performed preoperatively and postoperatively, and post-tympanoplasty tympanogram was also conducted to estimate the condition of middle ear cavity. Of the 143 patients, there were 73 patients with normal middle ear mucosa and 70 patients with abnormal middle ear mucosa around Eustachian tube opening. The mean ABG of subjects with normal middle ear mucosa was 20.1 dB preoperatively, and 9.7 dB postoperatively (p < 0.001). Preoperative mean ABG was 22.4 dB and postoperative mean ABG was 16.4 dB in abnormal middle ear mucosa group (p = 0.137). Postoperative ABGs for 500 and 1000 Hz (7.1, 7.7 dB) in normal middle ear mucosa patients were significantly lower than those (17.2, 19.4 dB) in abnormal middle ear mucosa patients (p < 0.001). There was statistically significant correlation between middle ear mucosa status and post-tympanoplasty audiologic outcomes. The better condition of middle ear ventilation, the better postoperative hearing thresholds revealed after tympanoplasty.

Transmission Electron Microscopy and Scanning Transmission X-Ray Microscopy Studies on the Bioaccumulation and Tissue Level Absorption of TiO2 Nanoparticles in Daphnia magna.

In this study, bioaccumulation and tissue-level absorption of TiO2 nanoparticles (NPs) in freshwater invertebrates were investigated using transmission electron microscopy (TEM) and scanning transmission X-ray microscopy (STXM). The TiO2 NPs were used to test impacts of core sizes (i.e., 5 ± 2 nm and 23 ± 7 nm for TiO2(SYN) and TiO2(P25), respectively) and agglomerations (i.e., well dispersed vs. highly agglomerated) on the uptake of TiO2 NPs in Daphnia magna (D. magna). Highly agglomerated TiO2 NPs, regardless of their core sizes, were heavily taken up into the digestive tract of D. magna and no detectable penetration of both TiO2 NPs into the gut epithelial cells of D. magna was observed in TEM and STXM images. However, significant damages involving morphological changes in the microvilli and gut epithelial cells (e.g., irregular shaped microvilli, epithelial cell protrusion, and dilatation of cytoplasmic inclusion) were observed only with the commercial TiO2 NPs (TiO2(P25)) with larger core size and mixed crystalline phase, while the laboratory synthesized TiO2 NPs (TiO2(Syn)) with smaller core size and single crystalline phase showed slight morphological changes in the gut microvilli and epithelial cells. In the case of D. magna exposed to the well dispersed synthetic TiO2 NP ((Cit)TiO2(Syn)), only a negligible amount of TiO2 NPs were found within the digestive tract of the D. magna without any significant damages in the gut microvilli and epithelial cells and any detectable penetrations of TiO2 NPs into epithelial cells of D. magna gut. These TEM and STXM observations confirmed us that uptake of NP into D. magna are strongly dependent on their agglomeration (i.e., hydrodynamic sizes), rather than their core sizes, while direct penetration of NPs into tissues of digestive tract seems unlikely without significant morphological changes (e.g., collapse of the epithelial tissue) caused by high toxicity of NPs or released metal ions.

Optimal Incorporation Level of Dietary Alternative Phosphate (MgHPO4) and Requirement for Phosphorus in Juvenile Far Eastern Catfish (Silurus asotus).

A growth trial was conducted to determine the optimal incorporation level of dietary magnesium hydrogen phosphate (MHP, MgHPO4), which was manufactured from swine manure and phosphorus (P), required by juvenile far eastern catfish (Silurus asotus). Graded MHP of 0.5%, 1.0%, 1.5%, and 2.0%, and 2.0% monocalcium phosphate (MCP) each was added to the basal diet (control) in lieu of cellulose to become the range of available P (AP) from 0.4% to 0.8% of which diets were designated as control, MHP0.5, MHP1.0, MHP1.5, MHP2.0, and MCP, respectively. Control diet contained fish meal (20%), soybean meal (40%), wheat flour (27%), corn gluten meal (5%), fish oil (2%) and soy oil (2%) as main ingredients. Following a 24 h fasting, 540 fish with a mean body weight of 11.8 g were randomly allotted to 6 groups in triplicate, whereby 18 tanks (0.4×0.6×0.36 cm, water volume of 66 L) were prepared. The feeding experiment lasted for 8 weeks. Fish group fed the control diet showed the lowest weight gain (WG) and feed efficiency (FE) among treatments. The WG was, however, not significantly different (p>0.05) from that of fish group fed MHP0.5. Fish group fed MHP2.0 showed the highest WG and FE of which values were not significantly different from those of fish groups fed diets MHP1.0 and MHP1.5 as well as MCP (p>0.05) except fish groups fed control and MHP0.5. Aspartate aminotransferase was significantly decreased with an increase in available P, while alanine aminotransferase did not show a significant difference among treatment. The highest inorganic P in plasma was observed in fish fed MHP2.0. From the present results, a second-order regression analysis revealed that the optimal dietary MHP level and the AP requirement were found to be 1.62% and 0.7%, respectively.