Lignin microcapsules prepared on the basis of flexible skeleton with high foliar retention and UV shielding properties.

Lignin-based microcapsules are extremely attractive for their biodegradability and photolysis resistance. However, the water-soluble all-lignin shells were unsatisfactory in terms of rainfall and foliar retention, and lacked the test of agricultural production practices. Herein, a novel microcapsule based on a flexible skeleton formed by interfacial polymerization and absorbed with lignin particles (LPMCs) was prepared in this study. Further analysis demonstrated that the shell was formed by cross-linking the two materials in layers and showed excellent flexibility and photolysis resistance. The pesticide loaded LPMCs showed about 98.68 % and 73.00 % improvement in scour resistance and photolysis resistance, respectively, as compared to the bare active ingredient. The foliar retention performance of LPMCs was tested in peanut plantations during the rainy season. LPMCs loaded with pyraclostrobin (Pyr) and tebuconazole (Teb) exhibited the best foliar disease control and optimum plant architecture, resulting in an increase in yield of about 5.36 %. LPMCs have a promising application prospect in the efficient pesticide utilization, by controlling its deformation, adhesion and release, an effective strategy for controlling diseases and managing plant growth was developed.

A multidimensional optimization strategy of pyraclostrobin-loaded microcapsules to improve the selectivity between toxicological risk in zebrafish and efficacy in controlling rice blast.

Developing microcapsules (MCs) delivery systems can effectively mitigate toxicological risk of highly active/toxic pesticides; whereas the controlled release functions also limiting their practical effectiveness. Therefore, designing a precise regulating strategy to balance the toxicity and bioactivity of MCs is urgently needed. Here, we prepared a series of pyraclostrobin-loaded MCs with different wall materials, particle sizes, core density and shell compactness using interfacial polymerization. The results showed that the MCs released more slowly in water with increasing particle sizes and capsule compactness, and they sunk more quickly with the increasing particle sizes and core density. Additionally, MCs with slower release speed was always accompanied with lower acute toxicity levels to zebrafish. When the release dynamics slowed down to the threshold dose on demand for disease control, facilitating settlement of MCs can further reduce toxicity within spatial and temporal dimensions. The poor accumulation of MCs with larger particle sizes or dense shell in gills was closely related to their efficient detoxification. Importantly, seven of the MCs samples possessed superior selectivity between bio-performance in controlling rice blast and toxicological hazard to fish compared to commercial formulations. The results provide a comprehensive guidance for developing an efficient and safe pesticide delivery system.

Amphiphilicity-Driven Small Alcohols Regulate the Flexibility of Pesticide-Loaded Microcapsules for Better Foliar Adhesion and Utilization.

The foliar loss of pesticides causes serious utilization decline and environmental risk. On the basis of biomimetics, pesticide-loaded microcapsules (MCs) with spontaneous deformation on foliar micro/nanostructures, like the snail suction cup, are prepared by interfacial polymerization. By controlling the usage or types of small alcohols in the MC preparation system, the flexibility of MCs is tunable. Through the investigation of emulsions and MC structures, we discover that the migration and distribution of small alcohols driven by amphiphilicity affect the process of interfacial polymerization between polyethylene glycol and 4,4-methylenediphenyl diisocyanate. By hydrophobic modification of the polymer and competition for oil monomers of small alcohols, the thickness and compactness of shells are reduced, whereas the density of the core is increased. As a result of the regulation in structures, the flexibility of MCs is improved significantly. In particularly, the MCs-N-pentanol (0.1 mol kg-1) with the best flexibility show strong scouring resistance on varied foliar structures, sustained release property on the air/solid interface, and persistent control effect against foliar diseases. The pesticide-loaded soft MCs provide an effective way to improve pesticide foliar utilization.

Emamectin benzoate nanogel suspension constructed from poly(vinyl alcohol)-valine derivatives and lignosulfonate enhanced insecticidal efficacy.

To reduce the negative impact of nanopesticide carriers of on the environment, a greener nanodelivery system is necessary. Nanogels are nontoxic and degradable carriers, however, the potential of nanogels for delivering pesticides has not been proven. In this study, poly(vinyl alcohol)-valine, an ecofriendly polymer, was synthesized and used to fabricate emamectin benzoate nanogel suspension (EB NS). The nanoformulation showed favorable stability at low temperature, high temperature or one year storage, and in water with different hardnesses. The retention of the EB NS solution on leaves was higher than that of an EB emulsifiable concentrate (EC) by approximately 9% at a concentration of 10 mg L-1. The half-life of EB nanogels under Ultra Violet irradiation was prolonged by 3.3-fold. Moreover, the bioactivity of the EB NS against Plutella xylostella was higher than that of the EB EC. These advantages resulted in a relatively long duration of pest control. The response of nanogels to laccase, a digestive enzyme in the digestive tract of lepidopteran pests, enables pesticide release on demand. Nanogels have the advantages of being ecofriendly carriers, exhibiting higher utilization, and prolonged pest control periods, and they have a brilliant future in pesticide delivery.

Using a reactive emulsifier to construct simple and convenient nanocapsules loaded with lambda-cyhalothrin to achieve efficient foliar delivery and insecticidal synergies.

Nanocapsules are a promising controlled release formulation for foliar pest control. However, the complicated process and high cost limit widespread use in agriculture, so a simpler and more convenient preparation system is urgently needed. Meanwhile, under complex field conditions, the advantageous mechanism of the nanosize effect and sustained release have no quantitative and detailed study. In this study, a reactive emulsifier (OP-10) is used to participate in the interfacial polymerization of the nanoemulsion, and polymer nanocapsules loaded with lambda-cyhalothrin (NCS@LC) are quickly and easily prepared to study the efficacy and synergistic mechanism of foliar pest control. As a result, the nanocapsule is about 150 nm with a stable core-shell structure. The nanoscale state increases the distribution and adhesion of the particles on the leaf surface, which increases the contact efficiency of pesticides under the different physiological stages and behavioral activities of the target organism. The shell structure provides sustained release characteristics and increases the UV resistance by about 2.5 times for pesticides. Compared with microcapsules loaded with lambda-cyhalothrin (CS@LC), NCS@LC not only shows rapid and synergistic insecticidal efficacy but also provides sustained insecticidal efficacy. The mortality of NCS is 3.4 times that of the nanosized emulsion in water (NEW) at the lowest concentration (0.5 mg L-1), and the control efficacy remained 77.3% after 7 days. Compared with NEW, NCS@LC provides excellent field efficacy, while LC50 for zebrafish is only 0.68 times without increasing the aquatic toxicity risk.

Self-Assembled Degradable Nanogels Provide Foliar Affinity and Pinning for Pesticide Delivery by Flexibility and Adhesiveness Adjustment.

At present, it is highly important to develop a simple and compatible nano delivery system for pesticides for foliar application, which can improve their insecticidal efficacy and resistance to adverse climates while reducing the environmental risks. Polyethylene glycol and 4,4-methylenediphenyl diisocyanate are used as hydrophilic soft and hydrophobic hard segments, respectively, for polymer self-assembly and polyurethane gelation in a nanoreactor. The nanocarrier synthesis and the pesticide loading are realized by a one-step integration procedure and suited well for hydrophobic active compounds. Modifying the molecular structure of the soft segment can adjust the flexibility of the nanocarriers and result in viscosity and deformation characteristics. After foliar spray application, the foliar flattening state of the nanogels increases the foliar protection area by 2.21 times and improves both pesticide exposure area and target contact efficiency. Concurrently, the flexibility and viscosity of the nanogels increase the washing resistance and the retention rate of the pesticide by approximately 80 times under continuous washing. The encapsulation of the nanogels reduces the foliar ultraviolet (UV) degradation and aquatic pesticide exposure, which increase the security of λ-cyhalothrine by 9.33 times. Moreover, the degradability of nanogels is beneficial for pesticide exposure and reducing pollution. This system has simple preparation, good properties, and environmental friendliness, making the nanocarriers promising for delivering pesticides.

Improving the efficacy against crop foliage disease by regulating fungicide adhesion on leaves with soft microcapsules.

BACKGROUND: Increasing pesticide retention on crop leaves is a key approach for guaranteeing efficacy when products are applied to foliage. Evidently, the formulation plays an important role in this process. Microcapsules (MCs) are a promising formulation, but whether and how their adhesion to the leaf surface affects retention and efficacy is not well understood. RESULTS: In this study, we found that the incorporation of polyethylene glycol (PEG) with different molecular weights into the MC shell affects the release profile of MCs and the contact area of these MCs to leaves by changing their softness. The cumulative release rates of pyraclostrobin (Pyr) MCs fabricated with PEG200, PEG400, PEG800 and PEG1500 were 80.61%, 90.98%, 94.07% and 97.40%, respectively. Scanning electron microscopy observations showed that the flexibility of the MCs increased with increasing PEG molecular weight. The median lethal concentration (LC50 ) of the MCs with different PEG to the zebrafish were 12.10, 8.10, 3.90 and 1.46 mg L-1 , respectively, which also indirectly reflected their release rate. Rainwater had less influence on the retention of the MCs prepared with PEG1500 than with the other PEG, which indicates a better adhesion to the target leave surfaces. MCs with the highest residual efficacy had better control efficacy on peanut leaf spot in field trials. CONCLUSION: Overall, adding PEG with an appropriate molecular weight to the MC shell can regulate the structure of the MC shell to improve the affinity between the MCs and leaves, which further improves the utilization of pesticides and reduces the environmental risks of pesticides. © 2021 Society of Chemical Industry.

Breast mass detection in digital mammography based on anchor-free architecture.

BACKGROUND AND OBJECTIVE: Accurate detection of breast masses in mammography images is critical to diagnose early breast cancer, which can greatly improve the patients' survival rate. However, it is still a big challenge due to the heterogeneity of breast masses and the complexity of their surrounding environment. Therefore, how to develop a robust breast mass detection framework in clinical practical applications to improve patient survival is a topic that researchers need to continue to explore. METHODS: To address these problems, we propose a one-stage object detection architecture, called Breast Mass Detection Network (BMassDNet), based on anchor-free and feature pyramid which makes the detection of breast masses of different sizes well adapted. We introduce a truncation normalization method and combine it with adaptive histogram equalization to enhance the contrast between the breast mass and the surrounding environment. Meanwhile, to solve the overfitting problem caused by small data size, we propose a natural deformation data augmentation method and mend the train data dynamic updating method based on the data complexity to effectively utilize the limited data. Finally, we use transfer learning to assist the training process and to improve the robustness of the model ulteriorly. RESULTS: On the INbreast dataset, each image has an average of 0.495 false positives whilst the recall rate is 0.930; On the DDSM dataset, when each image has 0.599 false positives, the recall rate reaches 0.943. CONCLUSIONS: The experimental results on datasets INbreast and DDSM show that the proposed BMassDNet can obtain competitive detection performance over the current top ranked methods.

Achieving Win-Win Ecotoxicological Safety and Fungicidal Activity of Pyraclostrobin-Loaded Polyurea Microcapsules by Selecting Proper Polyamines.

The fungicide pyraclostrobin is highly toxic to aquatic organisms. Microencapsulation is an effective way to reduce the exposure of pyraclostrobin to aquatic organisms but it also reduces the contact probability between the fungicide and plant pathogens. Hence, winning a balance between the toxicity and bioactivity of pyraclostrobin is very necessary. In this study, triethylenetetramine (TETA), ethylenediamine (EDA), hexamethylenediamine (HAD), and isophoronediamine (IPDA) were selected as cross-linkers to prepare the pyraclostrobin-loaded polyurea microcapsules (PU-MCs) by interfacial polymerization. TETA formed the shells with the highest degree of cross-linking, the slowest release profile, and the best protection against ultraviolet (UV). In terms of MCs fabricated by diamines, higher leaking, weaker UV resistance of the shells was observed with increasing carbon skeleton. TETA-MCs showed the highest safety to zebrafish (LC50 of 10.086 mg/L), whereas EDA-MCs, HAD-MCs, and IPDA-MCs were 5.342, 3.967, and 0.767 mg/L, respectively. TETA-MCs had the best long-term disease management, while the control efficacies of other MCs were higher at the early stage of disease development. Overall, a balance between the aquatic toxicities and fungicidal activities of pyraclostrobin-loaded PU-MCs could be reached through a simple selection of polyamines in the fabrication.

A new data augmentation method based on local image warping for medical image segmentation.

PURPOSE: The segmentation accuracy of medical images was improved by increasing the number of training samples using a local image warping technique. The performance of the proposed method was evaluated in the segmentation of breast masses, prostate and brain tumors, and lung nodules. METHODS: We propose a simple data augmentation method which is called stochastic evolution (SE). Specifically, the idea of SE stems from our thinking about the deterioration of the diseased tissue and the healing process. In order to simulate this natural process, we implement it according to the local distortion algorithm in image warping. In other words, the irregular deterioration and healing processes of the diseased tissue is simulated according to the direction of the local distortion, thereby producing a natural sample that is indistinguishable by humans. RESULTS: The proposed method is evaluated on four segmentation tasks of breast masses, prostate, brain tumors, and lung nodules. Comparing the experimental results of four segmentation methods based on the UNet segmentation architecture without adding any expanded data during training, the accuracy and the Hausdorff distance obtained in our approach remain almost the same as other methods. However, the dice similarity coefficient (DSC) and sensitivity (SEN) have both improved to some extent. Among them, DSC is increased by 5.2%, 2.8%, 1.0%, and 3.2%, respectively; SEN is increased by 6.9%, 4.3%, 1.2%, and 4.5%, respectively. CONCLUSIONS: Experimental results show that the proposed SE data augmentation method could improve the segmentation accuracy of breast masses, prostate, brain tumors, and lung nodules. The method also shows the robustness with different image datasets and imaging modalities.

Multi-model Ensemble Learning Architecture Based on 3D CNN for Lung Nodule Malignancy Suspiciousness Classification.

Classification of benign and malignant in lung nodules using chest CT images is a key step in the diagnosis of early-stage lung cancer, as well as an effective way to improve the patients' survival rate. However, due to the diversity of lung nodules and the visual similarity of lung nodules to their surrounding tissues, it is difficult to construct a robust classification model with conventional deep learning-based diagnostic methods. To address this problem, we propose a multi-model ensemble learning architecture based on 3D convolutional neural network (MMEL-3DCNN). This approach incorporates three key ideas: (1) Constructed multi-model network architecture can be well adapted to the heterogeneity of lung nodules. (2) The input that concatenated of the intensity image corresponding to the nodule mask, the original image, and the enhanced image corresponding to which can help training model to extract advanced feature with more discriminative capacity. (3) Select the corresponding model to different nodule size dynamically for prediction, which can improve the generalization ability of the model effectively. In addition, ensemble learning is applied in this paper to further improve the robustness of the nodule classification model. The proposed method has been experimentally verified on the public dataset, LIDC-IDRI. The experimental results show that the proposed MMEL-3DCNN architecture can obtain satisfactory classification results.

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection.

Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a critical step to have accurate detection of lung nodules in computed tomography (CT) images for the diagnosis of lung cancer. However, due to the heterogeneity of the lung nodules and the complexity of the surrounding environment, it is a challenge to develop a robust nodule detection method. In this study, we propose a two-stage convolutional neural networks (TSCNN) for lung nodule detection. The first stage based on the improved U-Net segmentation network is to establish an initial detection of lung nodules. During this stage, in order to obtain a high recall rate without introducing excessive false positive nodules, we propose a new sampling strategy for training. Simultaneously, a two-phase prediction method is also proposed in this stage. The second stage in the TSCNN architecture based on the proposed dual pooling structure is built into three 3D-CNN classification networks for false positive reduction. Since the network training requires a significant amount of training data, we designed a random mask as the data augmentation method in this study. Furthermore, we have improved the generalization ability of the false positive reduction model by means of ensemble learning. We verified the proposed architecture on the LUNA dataset in our experiments, which showed that the proposed TSCNN architecture did obtain competitive detection performance.

A cascaded dual-pathway residual network for lung nodule segmentation in CT images.

It is difficult to obtain an accurate segmentation due to the variety of lung nodules in computed tomography (CT) images. In this study, we propose a data-driven model, called the Cascaded Dual-Pathway Residual Network (CDP-ResNet) to improve the segmentation of lung nodules in the CT images. Our approach incorporates the multi-view and multi-scale features of different nodules from CT images. The proposed residual block based dual-path network extracts local features and rich contextual information of lung nodules. In addition, we designed an improved weighted sampling strategy to select training samples based on the edge. The proposed method was extensively evaluated on an LIDC dataset, which contains 986 nodules. Experimental results show that the CDP-ResNet achieves superior segmentation performance with an average DICE score (standard deviation) of 81.58% (11.05) on the LIDC dataset. Moreover, we compared our results with those of four radiologists on the same dataset. The comparison shows that the CDP-ResNet is slightly better than human experts in terms of segmentation accuracy. Meanwhile, the proposed segmentation method outperforms existing methods.

Bone Marrow Cells Detection: A Technique for the Microscopic Image Analysis.

In the detection of myeloproliferative, the number of cells in each type of bone marrow cells (BMC) is an important parameter for the evaluation. In this study, we propose a new counting method, which consists of three modules including localization, segmentation and classification. The localization of BMC is achieved from a color transformation enhanced BMC sample image and stepwise averaging method. In the nucleus segmentation, both stepwise averaging method and Otsu's method are applied to obtain a weighted threshold for segmenting the patch into nucleus and non-nucleus. In the cytoplasm segmentation, a color weakening transformation, an improved region growing method and the K-Means algorithm are employed. The connected cells with BMC will be separated by the marker-controlled watershed algorithm. The features will be extracted for the classification after the segmentation. In this study, the BMC are classified using the support vector machine into five classes; namely, neutrophilic split granulocyte, neutrophilic stab granulocyte, metarubricyte, mature lymphocytes and the outlier (all other cells not listed). Experimental results show that the proposed method achieves superior segmentation and classification performance with an average segmentation accuracy of 91.76% and an average recall rate of 87.49%. The comparison shows that the proposed segmentation and classification methods outperform the existing methods.

Tunable thermal, mechanical, and controlled-release properties of epoxy phenolic novolac resin microcapsules mediated by diamine crosslinkers.

Diverse shell structures can endow microcapsules (MCs) with a variety of properties. In this study, four kinds of MCs encapsulated with epoxy phenolic novolac resin (EPN) were obtained using diamines as crosslinkers. The FTIR and CLSM confirmed that the EPN-shell was successfully synthesized. SEM results suggested that all four of the MCs possessed smooth and imporous surfaces, and they displayed uniform size distribution, with the average size ranging from 11.2 to 14.8 µm. Specifically, the MCs synthesized with the crosslinkers of hexamethylene diamine (HAD) and isophorone diamine (IPDA) were more stable and could maintain their spherical shapes in a dry environment, while those synthesized with ethylene diamine (EDA) and triethylene tetramine (TETA) exhibited poorer thermal stabilities and faster release profiles. Lipophilic diamines can form thicker shell thickness, and have smaller d-space values and higher Young's modulus values. Therefore, the lipophilicity of the diamine agents is the key factor that influences the specific properties of the MCs. The hybrid approach was validated as a flexible strategy to meet diverse requirements. The present study can provide a comprehensive understanding of diamine crosslinkers in the synthesis of EPN-MCs, and such MCs can be extended to a wide range of fields because of their broad tunable performance.

Berberine inhibits EGFR signaling and enhances the antitumor effects of EGFR inhibitors in gastric cancer.

Cetuximab plus chemotherapy for advanced gastric cancer (GC) shows an active result in phase 2 trials. Unfortunately, Combination of cetuximab does not provide enough benefit to chemotherapy alone in phase 3 trials. Studies have demonstrated that berberine can suppress the activation of EGFR in tumors. In this study, we evaluated whether berberine could enhance the effects of EGFR-TKIs in GC cell lines and xenograft models. Our data suggest that berberine could effectively enhance the activity of erlotinib and cetuximab in vitro and in vivo. Berberine was found to inhibit growth in GC cell lines and to induce apoptosis. These effects were linked to inhibition of EGFR signaling activation, including the phosphorylation of STAT3. The expressions of Bcl-xL and Cyclind1 proteins were decreased, whereas the levels of cleavage of poly-ADP ribose polymerase (PARP) were considerably increased in the cell lines in response to berberine treatment. These results suggest a potential role for berberine in the treatment of GC, particularly in combination with EGFR-TKIs therapy. Berberine may be a competent therapeutic agent in GC where it can enhance the effects of EGFR inhibitors.

Promoter methylation of RNF180 is associated with H.pylori infection and serves as a marker for gastric cancer and atrophic gastritis.

Promoter methylation (PM) of RING-finger protein (RNF) 180 affects gastric cancer (GC) prognosis, but its association with risk of GC or atrophic gastritis (AG) is unclear. We investigated relationships between RNF180 PM and GC or AG, and the effects of Helicobactor pylori (H.pylori) infection on RNF180 PM. This study included 513 subjects (159 with GC, 186 with AG, and 168 healthy controls [CON]) for RNF180 PM analysis, and another 55 GC patients for RNF180 gene expression analysis. Methylation was quantified using average methylation rates (AMR), methylated CpG site counts (MSC) and hypermethylated CpG site counts (HSC). RNF180 promoter AMR and MSC increased with disease severity. Optimal cut-offs were GC + AG: AMR > 0.153, MSC > 4 or HSC > 1; GC: AMR > 0.316, MSC > 15 and HSC > 6. Hypermethylation at 5 CpG sites differed significantly between GC/AG and CON groups, and was more common in GC patients than AG and CON groups for 2 other CpG sites. The expression of RNF180 mRNA levels in tumor were significantly lower than those in non-tumor, with the same as in hypermethylation than hypomethylation group. H.pylori infection increased methylation in normal tissue or mild gastritis, and increased hypermethylation risk at 3 CpG sites in AG. In conclusion, higher AMR, MSC and HSC levels could identify AG + GC or GC. Some RNF180 promoter CpG sites could identify precancerous or early-stage GC. H.pylori affects RNF180 PM in normal tissue or mild gastritis, and increases hypermethylation in 3 CpG sites in AG.