Efficient Production of 9,22-Dihydroxy-23,24-bisnorchol-4-ene-3-one from Phytosterols by Modifying Multiple Genes in Mycobacterium fortuitum.

C19 steroids and C22 steroids are vital intermediates for the synthesis of steroid drugs. Compared with C19 steroids, C22 steroids are more suitable for synthesizing progesterone and adrenocortical hormones, albeit less developed. 9,22-dihydroxy-23,24-bisnorchol-4-ene-3-one(9-OHBA), due to its substituents at positions C-9 and C-22, is a beneficial and innovative steroid derivative for synthesizing corticosteroids. We focused on the C22 pathway in Mycobacterium fortuitum ATCC 35855, aiming to develop a productive strain that produces 9-OHBA. We used a mutant strain, MFΔkstD, that knocked out kstds from Mycobacterium fortuitum ATCC 35855 named MFKD in this study as the original strain. Hsd4A and FadA5 are key enzymes in controlling the C19 metabolic pathway of steroids in Mycobacterium fortuitum ATCC 35855. After knocking out hsd4A, MFKDΔhsd4A accumulated 81.47% 9-OHBA compared with 4.13% 9-OHBA in the strain MFKD. The double mutant MFKDΔhsd4AΔfadA5 further improved the selectivity of 9-OHBA to 95.13%, and 9α-hydroxy-4-androstenedione (9-OHAD) decreased to 0.90% from 4.19%. In the end, we obtained 6.81 g/L 9-OHBA from 10 g/L phytosterols with a molar yield of 80.33%, which showed the best performance compared with formerly reported strains.

Presence of Multiple Genetic Mutations Related to Insecticide Resistance in Chinese Field Samples of Two Phthorimaea Pest Species.

Potatoes hold the distinction of being the largest non-cereal food crop globally. The application of insecticides has been the most common technology for pest control. The repeated use of synthetic insecticides of the same chemical class and frequent applications have resulted in the emergence of insecticide resistance. Two closely related pests that feed on potato crops are the potato tuber moth, Phthorimaea operculella, and the tomato leafminer, Phthorimaea absoluta (syn. Tuta absoluta). Previous studies indicated the existence of insecticide resistance to various classes of insecticides including organophosphates, carbamates, and pyrethroids in field populations of P. operculella and P. absoluta. However, the exact mechanisms of insecticide resistance in P. operculella and to a lesser extent P. absoluta remain still poorly understood. Detecting resistance genotypes is crucial for the prediction and management of insecticide resistance. In this study, we identified multiple genetic mutations related to insecticide resistance in two species of Phthorimaea. An unexpected genetic divergence on target-site mutations was observed between P. operculella and P. absoluta. Three mutations (A201S, L231V, and F290V) in Ace1 (acetylcholinesterase), four mutations (M918T, L925M, T928I, and L1014F) in VGSC (voltage-gated sodium channel), and one mutation (A301S) in RDL (GABA-gated chloride channel) have been detected with varying frequencies in Chinese P. absoluta field populations. In contrast, P. operculella field populations showed three mutations (F158Y, A201S, and L231V) in Ace1, one mutation (L1014F) in VGSC at a lower frequency, and no mutation in RDL. These findings suggest that pyrethroids, organophosphates, and carbamates are likely to be ineffective in controlling P. absoluta, but not P. operculella. These findings contributed to a deeper understanding of the presence of target-site mutations conferring resistance to commonly used (and cheap) classes of insecticides in two closely related potato pests. It is recommended to consider the resistance status of both pests for the implementation of resistance management strategies in potatoes.

Supramolecular interactions between functional saccharide-based ionic liquids and cellulose macromolecules.

Interactions between polysaccharides and ionic liquids (ILs) at the molecular level are essential to elucidate the dissolution and/or plasticization mechanism of polysaccharides. Herein, saccharide-based ILs (SILs) were synthesized, and cellulose membrane was soaked in different SILs to evaluate the interactions between SILs and cellulose macromolecules. The relevant results showed that the addition of SILs into cellulose can effectively reduce the intra- and/or inter-molecular hydrogen bonds of polysaccharides. Glucose-based IL showed the intensest supramolecular interactions with cellulose macromolecules compared to sucrose- and raffinose-based ILs. Two-dimensional correlation and perturbation-correlation moving window Fourier transform infrared techniques were for the first time used to reveal the dynamic variation of the supramolecular interactions between SILs and cellulose macromolecules. Except for the typical HO⋯H interactions of cellulose itself, stronger -Cl⋯HO hydrogen bonding interactions were detected in the specimen of SILs-modified cellulose membranes. Supramolecular interactions of -Cl⋯H, HO⋯H, C-Cl⋯H, and -C=O⋯H between SILs and cellulose macromolecules sequentially responded to the stimuli of temperature. This work provides a new perspective to understanding the interaction mechanism between polysaccharides and ILs, and an avenue to develop the next-generation ILs for dissolving or thermoplasticizing polysaccharide materials.

Deep learning based decision tree ensembles for incomplete medical datasets.

BACKGROUND: In practice, the collected datasets for data analysis are usually incomplete as some data contain missing attribute values. Many related works focus on constructing specific models to produce estimations to replace the missing values, to make the original incomplete datasets become complete. Another type of solution is to directly handle the incomplete datasets without missing value imputation, with decision trees being the major technique for this purpose. OBJECTIVE: To introduce a novel approach, namely Deep Learning-based Decision Tree Ensembles (DLDTE), which borrows the bounding box and sliding window strategies used in deep learning techniques to divide an incomplete dataset into a number of subsets and learning from each subset by a decision tree, resulting in decision tree ensembles. METHOD: Two medical domain problem datasets contain several hundred feature dimensions with the missing rates of 10% to 50% are used for performance comparison. RESULTS: The proposed DLDTE provides the highest rate of classification accuracy when compared with the baseline decision tree method, as well as two missing value imputation methods (mean and k-nearest neighbor), and the case deletion method. CONCLUSION: The results demonstrate the effectiveness of DLDTE for handling incomplete medical datasets with different missing rates.

Photomechaelectric Nanogenerators with Different Photoisomers and Dipole Units for Harvesting UV Light Energy.

To date, transforming environmental energy into electricity through a non-mechanical way is challenging. Herein, a series of photomechaelectric (PME) polyurethanes containing azobenzene-based photoisomer units and ionic liquid-based dipole units are synthesized, and corresponding PME nanogenerators (PME-NGs) to harvest electricity are fabricated. The dependence of the output performance of PME-NGs on the structure of the polyurethane is evaluated. The results show that the UV light energy can directly transduce into alternating-current (AC) electricity by PME-NGs via a non-mechanical way. The optimal open-circuit voltage and short-circuit current of PME-NGs under UV illumination reach 17.4 V and 696 µA, respectively. After rectification, the AC electricity can be further transformed into direct-current (DC) electricity and stored in a capacitor to serve as a power system to actuate typical microelectronics. The output performance of PME-NGs is closely related to the hard segment content of the PME polyurethane and the radius of counter anions in the dipole units. Kelvin probe force microscopy is used to confirm the existence of the PME effect and the detailed mechanism about the generation of AC electricity in PME-NGs is proposed, referring to the back and forth drift of induced electrons on the two electrodes in contact with the PME polyurethanes.

Combining data discretization and missing value imputation for incomplete medical datasets.

Data discretization aims to transform a set of continuous features into discrete features, thus simplifying the representation of information and making it easier to understand, use, and explain. In practice, users can take advantage of the discretization process to improve knowledge discovery and data analysis on medical domain problem datasets containing continuous features. However, certain feature values were frequently missing. Many data-mining algorithms cannot handle incomplete datasets. In this study, we considered the use of both discretization and missing-value imputation to process incomplete medical datasets, examining how the order of discretization and missing-value imputation combined influenced performance. The experimental results were obtained using seven different medical domain problem datasets: two discretizers, including the minimum description length principle (MDLP) and ChiMerge; three imputation methods, including the mean/mode, classification and regression tree (CART), and k-nearest neighbor (KNN) methods; and two classifiers, including support vector machines (SVM) and the C4.5 decision tree. The results show that a better performance can be obtained by first performing discretization followed by imputation, rather than vice versa. Furthermore, the highest classification accuracy rate was achieved by combining ChiMerge and KNN with SVM.

Functional analysis of acyl-CoA dehydrogenases and their application to C22 steroid production.

Acyl-CoA dehydrogenase (ChsE) is involved in the steroid side-chain degradation process. However, their function in vivo remains unclear. In this study, three ChsE, ChsE1-ChsE2, ChsE3, and ChsE4-ChsE5, were identified in Mycolicibacterium neoaurum, and their functions in vivo are studied and compared with those from Mycobacterium tuberculosis in vitro. By gene knockout, complementation, and the bioconversion of phytosterols, the function of ChsE was elucidated that ChsE4-ChsE5 could utilize C27, C24, and C22 steroids in vivo. ChsE3 could utilize C27 and C24 steroids in vivo. ChsE1-ChsE2 could utilize C27, C24, and C22 steroids in vivo. What is more, the production strain of a C22 steroid, 3-oxo-4,17-pregadiene-20-carboxylic acid methyl ester (PDCE), is constructed with ChsE overexpression. This study improved the understanding of the steroid bioconversion pathway and proposed a method of the production of a new C22 steroid. KEY POINTS: • Three ChsE paralogs from M. neoaurum are identified and studied. • The function of ChsE is overlapped in vivo. • A C22 steroid (PDCE) producer was constructed with ChsE overexpression.

Multifunctional biological properties and phytochemical constituents of Mangifera indica L. seed kernel extract for preventing skin aging.

This study aimed to investigate the potential of Mangifera indica L. seed kernel extract, which is highly discarded by the global food processing industry, as a multifunctional bioactive ingredient for nutraceutical and cosmeceutical applications. Different extracting solvents were utilized, the extracts were then tested for their antioxidant activities using DPPH, ABTS radical scavenging assays, and inhibition of lipid peroxidation. Additionally, total phenolic content (TPC), total flavonoid content (TFC), and gallic acid content were elucidated using Folin-Ciocalteu and aluminum chloride colorimetric assays, as well as high performance liquid chromatography. The hydroethanolic extract (KMHE) exhibited the highest percentage yield, with the highest antioxidant activity owing to its high phenolic content. KMHE consisted of 773.66 ± 9.42 mg GAE/g extract in TPC, 36.20 ± 4.20 mg RU/g extract in TFC. Additionally, gallic acid was shown to be a major constituent of KMHE. KMHE was investigated for anti-tyrosinase, anti-hyaluronidase, anti-MMP-2, and anti-MMP-9 activities. Moreover, the anti-inflammatory effects of KMHE were studied in RAW 264.7 cells induced by nitric oxide and KMHE was shown to prevent DNA damage, indicating an inhibitory effect on cellular aging. KMHE showed outstanding anti-tyrosinase activity and was as potent an anti-hyaluronidase as gallic acid. Additionally, our results reveal notable anti-MMP-2 and anti-MMP-9 effects that were not significantly different from those of gallic acid. Furthermore, KMHE demonstrated 61.54 ± 2.39% nitric oxide inhibition, with no cytotoxic effects, in RAW264.7 cells, and also prevented DNA damage in the human fibroblast BJ cell line with no cytotoxic effects. Therefore, KMHE could be a promising, natural multifunctional bioactive compound for nutraceutical and cosmeceutical applications.

Effects of Carissa carandas Linn. Fruit, Pulp, Leaf, and Seed on Oxidation, Inflammation, Tyrosinase, Matrix Metalloproteinase, Elastase, and Hyaluronidase Inhibition.

In this study, the potential of Carissa carandas Linn. as a natural anti-aging, antioxidant, and skin whitening agent was studied. Various parts of C. carandas, including fruit, leaf, seed, and pulp were sequentially extracted by maceration using n-hexane, ethyl acetate, and ethanol, respectively. High-performance liquid chromatography, Folin-Ciocalteu, and Dowd method were used to investigate their chemical compositions. The inhibitory activities of oxidation process, matrix metalloproteinases (MMPs), elastase, hyaluronidase, and tyrosinase were analyzed. Cytotoxicity was determined by 3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyl tetrazolium bromide assay in a human epidermal keratinocyte line (HaCaT). The results exhibited that ethyl acetate could extract the most ursolic acid from C. carandas, while ethanol could extract the most phenolics and flavonoids. The leaf extract had the highest content of ursolic acid, phenolics, and flavonoids. The leaf extracted with ethyl acetate (AL) had the highest ursolic acid content (411.8 mg/g extract) and inhibited MMP-1, NF-kappa B, and tyrosinase activity the most. Ursolic acid has been proposed as a key component in these biological activities. Although several C. carandas extracts are beneficial to human skin, AL has been proposed for use in cosmetics and cosmeceuticals due to its superior anti-wrinkle, anti-inflammation, and whitening properties.

Chemical Constituents, Antioxidant, Anti-MMPs, and Anti-Hyaluronidase Activities of Thunbergia laurifolia Lindl. Leaf Extracts for Skin Aging and Skin Damage Prevention.

This study aimed to investigate the potential usage of Thunbergia laurifolia Lindl. leaf extracts in the cosmetic industry. Matrix metalloproteinases (MMPs) and hyaluronidase inhibition of T. laurifolia leaf extracts, prepared using reflux extraction with deionized water (RE) and 80% v/v ethanol using Soxhlet's apparatus (SE), were determined. Rosmarinic acid, phenolics, and flavonoids contents were determined using high-performance liquid chromatography, Folin-Ciocalteu, and aluminum chloride colorimetric assays, respectively. Antioxidant activities were determined by 1,1-diphenyl-2-picrylhydrazyl (DPPH) and linoleic acid-thiocyanate assays. MMP-1 inhibition was investigated using enzymatic and fluorescent reactions, whereas MMP-2, MMP-9, and hyaluronidase inhibition were investigated using gel electrophoresis. Cytotoxicity on human fibroblast cell line was also investigated. The results demonstrated that SE contained significantly higher content of rosmarinic acid (5.62% ± 0.01%) and flavonoids (417 ± 25 mg of quercetin/g of extract) but RE contained a significantly higher phenolics content (181 ± 1 mg of gallic acid/g of extract; p < 0.001). SE possessed higher lipid peroxidation inhibition but less DPPH• scavenging activity than RE. Both extracts possessed comparable hyaluronidase inhibition. SE was as potent an MMP-1 inhibitor as gallic acid (half maximal inhibitory concentration values were 12.0 ± 0.3 and 8.9 ± 0.4 mg/cm3, respectively). SE showed significantly higher MMP-2 and MMP-9 inhibition than RE (p < 0.05). Therefore, SE is a promising natural anti-ageing ingredient rich in rosmarinic acid and flavonoids with antioxidant, anti-hyaluronidase, and potent MMPs inhibitory effects that could be applied in the cosmetic industry.

Dermal Delivery Enhancement of Natural Anti-Ageing Compounds from Ocimum sanctum Linn. Extract by Nanostructured Lipid Carriers.

This study aimed to develop nanodelivery systems for enhancing the Ocimum sanctum Linn. extract delivery into the skin. Rosmarinic acid (RA) was used as a marker for the quantitative determination of the extract by high-performance liquid chromatography. Nanostructured lipid carriers (NLC), nanoemulsion, liposome, and niosome, were developed and characterized for internal droplet size, polydispersity index (PDI), and zeta potential using photon correlation spectroscopy. Irritation properties of each formulations were investigated by hen's egg test on the chorioallantoic membrane. In vitro release, skin permeation, and skin retention are determined. NLC was suggested as the most suitable system since it enhances the dermal delivery of RA with the significant skin retention amount of 27.1 ± 1.8% (p < 0.05). Its internal droplet size, PDI, and zeta potential were 261.0 ± 5.3 nm, 0.216 ± 0.042, and -45.4 ± 2.4 mV, respectively. RA released from NLC with a sustained release pattern with the release amount of 1.29 ± 0.15% after 24 h. NLC induced no irritation and did not permeate through the skin. Therefore, NLC containing O. sanctum extract was an attractive dermal delivery system that was safe and enhanced dermal delivery of RA. It was suggested for further used as topical anti-ageing products.

Outlier Removal in Model-Based Missing Value Imputation for Medical Datasets.

Many real-world medical datasets contain some proportion of missing (attribute) values. In general, missing value imputation can be performed to solve this problem, which is to provide estimations for the missing values by a reasoning process based on the (complete) observed data. However, if the observed data contain some noisy information or outliers, the estimations of the missing values may not be reliable or may even be quite different from the real values. The aim of this paper is to examine whether a combination of instance selection from the observed data and missing value imputation offers better performance than performing missing value imputation alone. In particular, three instance selection algorithms, DROP3, GA, and IB3, and three imputation algorithms, KNNI, MLP, and SVM, are used in order to find out the best combination. The experimental results show that that performing instance selection can have a positive impact on missing value imputation over the numerical data type of medical datasets, and specific combinations of instance selection and imputation methods can improve the imputation results over the mixed data type of medical datasets. However, instance selection does not have a definitely positive impact on the imputation result for categorical medical datasets.

Composition and Bioactivity of Essential Oil from Citrus grandis (L.) Osbeck 'Mato Peiyu' Leaf.

'Mato Peiyu' pomelo (Citrus grandis (L.) Osbeck 'Mato Peiyu') leaves from pruning are currently an agricultural waste. The aim of this study was to isolate essential oils from these leaves through steam distillation (SD) and solvent-free microwave extraction (SFME) and to evaluate their applicability to skin care by analyzing their antimicrobial, antioxidant (diphenyl-1-picrylhydrazyl scavenging assay, ß-carotene/linoleic acid assay, and nitric oxide scavenging assay), anti-inflammatory (5-lipoxygenase inhibition assay), and antityrosinase activities. The gas chromatography-mass spectrometry results indicated that the main components of 'Mato Peiyu' leaf essential oils were citronellal and citronellol, with a total percentage of 50.71% and 59.82% for SD and SFME, respectively. The highest bioactivity among all assays was obtained for 5-lipoxygenase inhibition, with an IC50 value of 0.034% (v/v). The MIC90 of the antimicrobial activity of essential oils against Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Candida albicans ranged from 0.086% to 0.121% (v/v). Citronellal and citronellol were the main contributors, accounting for at least 54.58% of the essential oil's bioactivity. This paper is the first to report the compositions and bioactivities of 'Mato Peiyu' leaf essential oil, and the results imply that the pomelo leaf essential oil may be applied in skin care.

SVM and SVM Ensembles in Breast Cancer Prediction.

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary to decide the kernel function, and different kernel functions can result in different prediction performance. However, there have been very few studies focused on examining the prediction performances of SVM based on different kernel functions. Moreover, it is unknown whether SVM classifier ensembles which have been proposed to improve the performance of single classifiers can outperform single SVM classifiers in terms of breast cancer prediction. Therefore, the aim of this paper is to fully assess the prediction performance of SVM and SVM ensembles over small and large scale breast cancer datasets. The classification accuracy, ROC, F-measure, and computational times of training SVM and SVM ensembles are compared. The experimental results show that linear kernel based SVM ensembles based on the bagging method and RBF kernel based SVM ensembles with the boosting method can be the better choices for a small scale dataset, where feature selection should be performed in the data pre-processing stage. For a large scale dataset, RBF kernel based SVM ensembles based on boosting perform better than the other classifiers.

Research performance of AACSB accredited institutions in Taiwan: before versus after accreditation.

INTRODUCTION: More and more universities are receiving accreditation from the Association to Advance Collegiate Schools of Business (AACSB), which is an international association for promoting quality teaching and learning at business schools. To be accredited, the schools are required to meet a number of standards ensuring that certain levels of teaching quality and students' learning are met. However, there are a variety of points of view espoused in the literature regarding the relationship between research and teaching, some studies have demonstrated that research and teaching these are complementary elements of learning, but others disagree with these findings. CASE DESCRIPTION: Unlike past such studies, we focus on analyzing the research performance of accredited schools during the period prior to and after receiving accreditation. The objective is to answer the question as to whether performance has been improved by comparing the same school's performance before and after accreditation. In this study, four AACSB accredited universities in Taiwan are analyzed, including one teaching oriented and three research oriented universities. Research performance is evaluated by comparing seven citation statistics, the number of papers published, number of citations, average number of citations per paper, average citations per year, h-index (annual), h-index, and g-index. DISCUSSION AND EVALUATION: The analysis results show that business schools demonstrated enhanced research performance after AACSB accreditation, but in most accredited schools the proportion of faculty members not actively doing research is larger than active ones. CONCLUSION: This study shows that the AACSB accreditation has a positive impact on research performance. The findings can be used as a reference for current non-accredited schools whose research goals are to improve their research productivity and quality.

On mining incomplete medical datasets: Ordering imputation and classification.

BACKGROUND: To collect medical datasets, it is usually the case that a number of data samples contain some missing values. Performing the data mining task over the incomplete datasets is a difficult problem. In general, missing value imputation can be approached, which aims at providing estimations for missing values by reasoning from the observed data. Consequently, the effectiveness of missing value imputation is heavily dependent on the observed data (or complete data) in the incomplete datasets. OBJECTIVE: In this paper, the research objective is to perform instance selection to filter out some noisy data (or outliers) from a given (complete) dataset to see its effect on the final imputation result. Specifically, four different processes of combining instance selection and missing value imputation are proposed and compared in terms of data classification. METHODS: Experiments are conducted based on 11 medical related datasets containing categorical, numerical, and mixed attribute types of data. In addition, missing values for each dataset are introduced into all attributes (the missing data rates are 10%, 20%, 30%, 40%, and 50%). For instance selection and missing value imputation, the DROP3 and k-nearest neighbor imputation methods are employed. On the other hand, the support vector machine (SVM) classifier is used to assess the final classification accuracy of the four different processes. RESULTS: The experimental results show that the second process by performing instance selection first and imputation second allows the SVM classifiers to outperform the other processes. CONCLUSIONS: For incomplete medical datasets containing some missing values, it is necessary to perform missing value imputation. In this paper, we demonstrate that instance selection can be used to filter out some noisy data or outliers before the imputation process. In other words, the observed data for missing value imputation may contain some noisy information, which can degrade the quality of the imputation result as well as the classification performance.

An efficient data preprocessing approach for large scale medical data mining.

BACKGROUND: The size of medical datasets is usually very large, which directly affects the computational cost of the data mining process. Instance selection is a data preprocessing step in the knowledge discovery process, which can be employed to reduce storage requirements while also maintaining the mining quality. This process aims to filter out outliers (or noisy data) from a given (training) dataset. However, when the dataset is very large in size, more time is required to accomplish the instance selection task. OBJECTIVE: In this paper, we introduce an efficient data preprocessing approach (EDP), which is composed of two steps. The first step is based on training a model over a small amount of training data after preforming instance selection. The model is then used to identify the rest of the large amount of training data. METHODS: Experiments are conducted based on two medical datasets for breast cancer and protein homology prediction problems that contain over 100000 data samples. In addition, three well-known instance selection algorithms are used, IB3, DROP3, and genetic algorithms. On the other hand, three popular classification techniques are used to construct the learning models for comparison, namely the CART decision tree, k-nearest neighbor (k-NN), and support vector machine (SVM). RESULTS: The results show that our proposed approach not only reduces the computational cost by nearly a factor of two or three over three other state-of-the-art algorithms, but also maintains the final classification accuracy. CONCLUSIONS: To perform instance selection over large scale medical datasets, it requires a large computational cost to directly execute existing instance selection algorithms. Our proposed EDP approach solves this problem by training a learning model to recognize good and noisy data. To consider both computational complexity and final classification accuracy, the proposed EDP has been demonstrated its efficiency and effectiveness in the large scale instance selection problem.

Antioxidant and Anti-Inflammatory Properties of Longan (Dimocarpus longan Lour.) Pericarp.

This study examined the antioxidant and anti-inflammatory activities of the water extract of longan pericarp (WLP). The results showed that WLP exhibited radical scavenging, reducing activity and liposome protection activity. In addition, WLP also inhibited lipopolysaccharide (LPS)-induced nitric oxide (NO) production in macrophages. Further, administration of WLP, in the range of 100-400 mg/kg, showed a concentration-dependent inhibition on paw edema development following carrageenan (Carr) treatment in mice. The anti-inflammatory effects of WLP may be related to NO and tumor necrosis factor (TNF-α) suppression and associated with the increase in the activities of antioxidant enzymes, including catalase, superoxide dismutase, and glutathione peroxidase. Overall, the results showed that WLP might serve as a natural antioxidant and inflammatory inhibitor.

Risk management strategy to increase the safety of workers in the nanomaterials industry.

In recent years, many engineered nanomaterials (NMs) have been produced, but increasing research has revealed that these may have toxicities far greater than conventional materials and cause significant adverse health effects. At present, there is insufficient data to determine the permissible concentrations of NMs in the workplace. There is also a lack of toxicity data and environmental monitoring results relating to complete health risk assessment. In view of this, we believe that workers in the NMs industry should be provided with simple and practical risk management strategy to ensure occupational health and safety. In this study, we developed a risk management strategy based on the precautionary risk management (PRM). The risk of the engineered NMs manufacturing plants can be divided into three levels based on aspect identification, solubility tests, dermal absorption, and cytotoxic analyses. The risk management strategies include aspects relating to technology control, engineering control, personal protective equipment, and monitoring of the working environment for each level. Here we report the first case in which a simple and practical risk management strategy applying in specific engineered NMs manufacturing plants. We are confident that our risk management strategy can be effectively reduced engineered NM industries risks for workers.