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1.
Colloids Surf B Biointerfaces ; 209(Pt 1): 112166, 2021 Oct 20.
Artigo em Inglês | MEDLINE | ID: mdl-34739877

RESUMO

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.

2.
Nanoscale ; 13(37): 15647-15658, 2021 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-34532728

RESUMO

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.


Assuntos
Inseticidas , Nanocápsulas , Piretrinas , Animais , Nanocápsulas/toxicidade , Nitrilas , Peixe-Zebra
3.
ACS Nano ; 15(9): 14598-14609, 2021 09 28.
Artigo em Inglês | MEDLINE | ID: mdl-34427447

RESUMO

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.


Assuntos
Praguicidas , Adesividade , Nanogéis
4.
Pest Manag Sci ; 77(10): 4418-4424, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-33991053

RESUMO

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.


Assuntos
Fungicidas Industriais , Praguicidas , Animais , Cápsulas , Fungicidas Industriais/farmacologia , Polímeros , Peixe-Zebra
5.
Comput Methods Programs Biomed ; 205: 106033, 2021 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-33845408

RESUMO

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.


Assuntos
Neoplasias da Mama , Mamografia , Mama/diagnóstico por imagem , Neoplasias da Mama/diagnóstico por imagem , Humanos
6.
J Agric Food Chem ; 69(7): 2099-2107, 2021 Feb 24.
Artigo em Inglês | MEDLINE | ID: mdl-33555871

RESUMO

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.


Assuntos
Poliaminas , Peixe-Zebra , Animais , Cápsulas , Polímeros , Estrobilurinas
7.
Med Phys ; 48(4): 1685-1696, 2021 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-33300190

RESUMO

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.


Assuntos
Processamento de Imagem Assistida por Computador , Imageamento por Ressonância Magnética , Algoritmos , Mama , Humanos , Masculino , Próstata
8.
J Digit Imaging ; 33(5): 1242-1256, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-32607905

RESUMO

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.


Assuntos
Neoplasias Pulmonares , Humanos , Pulmão , Neoplasias Pulmonares/diagnóstico por imagem , Aprendizado de Máquina , Interpretação de Imagem Radiográfica Assistida por Computador , Nódulo Pulmonar Solitário/diagnóstico por imagem , Tomografia Computadorizada por Raios X
9.
IEEE J Biomed Health Inform ; 24(7): 2006-2015, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-31905154

RESUMO

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.


Assuntos
Neoplasias Pulmonares/diagnóstico por imagem , Redes Neurais de Computação , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Humanos , Imageamento Tridimensional , Tomografia Computadorizada por Raios X/métodos
10.
Phys Med ; 63: 112-121, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31221402

RESUMO

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.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Neoplasias Pulmonares/diagnóstico por imagem , Redes Neurais de Computação , Tomografia Computadorizada por Raios X , Humanos
11.
J Med Syst ; 43(4): 82, 2019 Feb 23.
Artigo em Inglês | MEDLINE | ID: mdl-30798374

RESUMO

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.


Assuntos
Células da Medula Óssea/classificação , Processamento de Imagem Assistida por Computador/métodos , Microscopia/métodos , Reconhecimento Automatizado de Padrão/métodos , Máquina de Vetores de Suporte , Algoritmos , Núcleo Celular , Cor , Humanos
12.
Oncotarget ; 7(46): 76076-76086, 2016 Nov 15.
Artigo em Inglês | MEDLINE | ID: mdl-27738318

RESUMO

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.


Assuntos
Antineoplásicos/farmacologia , Berberina/farmacologia , Receptores ErbB/antagonistas & inibidores , Receptores ErbB/metabolismo , Inibidores de Proteínas Quinases/farmacologia , Transdução de Sinais/efeitos dos fármacos , Neoplasias Gástricas/metabolismo , Animais , Apoptose/efeitos dos fármacos , Pontos de Checagem do Ciclo Celular/efeitos dos fármacos , Linhagem Celular Tumoral , Proliferação de Células , Sobrevivência Celular/efeitos dos fármacos , Cetuximab/farmacologia , Modelos Animais de Doenças , Sinergismo Farmacológico , Humanos , Camundongos , Fosforilação , Neoplasias Gástricas/tratamento farmacológico , Neoplasias Gástricas/patologia , Ensaios Antitumorais Modelo de Xenoenxerto
13.
Oncotarget ; 7(17): 24800-9, 2016 Apr 26.
Artigo em Inglês | MEDLINE | ID: mdl-27050149

RESUMO

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.


Assuntos
Biomarcadores Tumorais/genética , Metilação de DNA , Gastrite Atrófica/microbiologia , Infecções por Helicobacter/genética , Neoplasias Gástricas/microbiologia , Ubiquitina-Proteína Ligases/genética , Ilhas de CpG , Progressão da Doença , Feminino , Gastrite Atrófica/genética , Humanos , Masculino , Pessoa de Meia-Idade , Prognóstico , Regiões Promotoras Genéticas , Neoplasias Gástricas/genética
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