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1.
Artigo em Inglês | MEDLINE | ID: mdl-35031972

RESUMO

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide with poor chemotherapeutic efficiency due to multidrug resistance (MDR); it is very important to develop a targeted nanocarrier for the treatment of HCC. In this study, a programmed death ligand 1 (PD-L1)-conjugated nanoliposome was constructed for co-delivery of paclitaxel (PTX) and P-glycoprotein (P-gp) inhibitor zosuquidar (ZSQ) to overcome MDR in human HCC cells and tumors in vivo. Transmission electron microscopy (TEM) and nanoparticle tracking analysis (NTA) were used to examine the nanoparticles morphology and size; PD-1-conjugated PTX and ZSQ-loaded nanoliposomes (PD-PZLP) revealed a spherical shape with a size of 139.5 ± 10.7 nm. Then, the physicochemical properties, as well as the drug loading capacity, release profile, cellular uptake, and cytotoxicity of the dual drug-encapsulated nanoliposomes were characterized. PD-PZLP displayed a high drug loading capacity of 20 ~ 30% for both PTX and ZSQ; the drug release of PTX and ZSQ in pH 5.0 was significantly faster than in pH 7.4. Cellular uptake study demonstrated PD-PZLP had higher internalization efficiency than non-targeted PZLP. Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) staining and reactive oxygen species (ROS) analysis demonstrated that PD-PZLP triggered an excessive ROS reaction and cell apoptosis compared with that of free PTX or ZSQ, which was also consistent with the cell antiproliferative effects in MTT assay. Furthermore, PD-PZLP could enhance synergistic antitumor effects on 7721/ADM xenograft tumor model, which also significantly alleviated hepatotoxicity as evident from the decreased aspartate transaminase (AST) and alanine transaminase (ALT) levels. Overall, PD-PZLP exhibited high loading capacity, significant synergistic effects, promising antitumor efficacy, and the lowest toxicity, which provide a promising strategy to overcome MDR in HCC.

2.
Sci Total Environ ; 805: 150292, 2022 Jan 20.
Artigo em Inglês | MEDLINE | ID: mdl-34536857

RESUMO

Since the loss of honeybees in hives could have a greater impact on colony health than those of their foraging bees, it is imperative to know beehives' pesticide exposure via oral ingestion of contaminated in-hive matrices. Here, a 4-year monitoring survey of 64 pesticide residues in pollen, nectar and related beehive matrices (beebread and honey) from China's main honey producing areas was carried out using a modified version of the QuEChERS multi-residue method. The results showed that 93.6% of pollen, 81.5% of nectar, 96.6% of beebread, and 49.3% of honey containing at least one target pesticide were detected either at or above the method detection limits (MDLs), respectively, with up to 19 pesticides found per sample. Carbendazim was the most frequently detected pesticide (present in >85% of the samples), and pyrethroids were also abundant (median concentration = 134.3-279.0 µg/kg). The transfer of pesticides from the environment into the beehive was shown, but the pesticide transference ratio may be affected by complex factors. Although the overall risk to colony health from pesticides appears to be at an acceptable level, the hazard quotient/hazard index (HQ/HI) value revealed that pyrethroids were clearly the most influential contributor, accounting for up to 45% of HI. Collectively, these empirical findings provide further insights into the extent of contamination caused by agricultural pesticide use on honeybee colonies.


Assuntos
Mel , Resíduos de Praguicidas , Praguicidas , Urticária , Animais , Abelhas , Mel/análise , Resíduos de Praguicidas/análise , Praguicidas/análise , Pólen/química
3.
Quant Imaging Med Surg ; 11(12): 4767-4780, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34888188

RESUMO

Background: Acquiring sparse-view cone-beam computed tomography (CBCT) is an effective way to reduce the imaging dose. However, images reconstructed by the conventional filtered back-projection method suffer from severe streak artifacts due to the projection under-sampling. Existing deep learning models have demonstrated feasibilities in restoring volumetric structures from the highly under-sampled images. However, because of the inter-patient variabilities, they failed to restore the patient-specific details with the common restoring pattern learned from the group data. Although the patient-specific models have been developed by training models using the intra-patient data and have shown effectiveness in restoring the patient-specific details, the models have to be retrained to be exclusive for each patient. It is highly desirable to develop a generalized model that can utilize the patient-specific information for the under-sampled image augmentation. Methods: In this study, we proposed a merging-encoder convolutional neural network (MeCNN) to realize the prior image-guided under-sampled CBCT augmentation. Instead of learning the patient-specific structures, the proposed model learns a generalized pattern of utilizing the patient-specific information in the prior images to facilitate the under-sampled image enhancement. Specifically, the MeCNN consists of a merging-encoder and a decoder. The merging-encoder extracts image features from both the prior CT images and the under-sampled CBCT images, and merges the features at multi-scale levels via deep convolutions. The merged features are then connected to the decoders via shortcuts to yield high-quality CBCT images. The proposed model was tested on both the simulated CBCTs and the clinical CBCTs. The predicted CBCT images were evaluated qualitatively and quantitatively in terms of image quality and tumor localization accuracy. Mann-Whitney U test was conducted for the statistical analysis. P<0.05 was considered statistically significant. Results: The proposed model yields CT-like high-quality CBCT images from only 36 half-fan projections. Compared to other methods, CBCT images augmented by the proposed model have significantly lower intensity errors, significantly higher peak signal-to-noise ratio, and significantly higher structural similarity with respect to the ground truth images. Besides, the proposed method significantly reduced the 3D distance of the CBCT-based tumor localization errors. In addition, the CBCT augmentation is nearly real-time. Conclusions: With the prior-image guidance, the proposed method is effective in reconstructing high-quality CBCT images from the highly under-sampled projections, considerably reducing the imaging dose and improving the clinical utility of the CBCT.

4.
Quant Imaging Med Surg ; 11(12): 4835-4846, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34888193

RESUMO

Background: Artificial intelligence (AI) based radiotherapy treatment planning tools have gained interest in automating the treatment planning process. It is essential to understand their overall robustness in various clinical scenarios. This is an existing gap between many AI based tools and their actual clinical deployment. This study works to fill the gap for AI based treatment planning by investigating a clinical robustness assessment (CRA) tool for the AI based planning methods using a phantom simulation approach. Methods: A cylindrical phantom was created in the treatment planning system (TPS) with the axial dimension of 30 cm by 18 cm. Key structures involved in pancreas stereotactic body radiation therapy (SBRT) including PTV25, PTV33, C-Loop, stomach, bowel and liver were created within the phantom. Several simulation scenarios were created to mimic multiple scenarios of anatomical changes, including displacement, expansion, rotation and combination of three. The goal of treatment planning was to deliver 25 Gy to PTV25 and 33 Gy to PTV33 in 5 fractions in simultaneous integral boost (SIB) manner while limiting luminal organ-at-risk (OAR) max dose to be under 29 Gy. A previously developed deep learning based AI treatment planning tool for pancreas SBRT was identified as the validation object. For each scenario, the anatomy information was fed into the AI tool and the final fluence map associated to the plan was generated, which was subsequently sent to TPS for leaf sequencing and dose calculation. The final auto plan's quality was analyzed against the treatment planning constraint. The final plans' quality was further analyzed to evaluate potential correlation with anatomical changes using the Manhattan plot. Results: A total of 32 scenarios were simulated in this study. For all scenarios, the mean PTV25 V25Gy of the AI based auto plans was 96.7% while mean PTV33 V33Gy was 82.2%. Large variation (16.3%) in PTV33 V33Gy was observed due to anatomical variations, a.k.a. proximity of luminal structure to PTV33. Mean max dose was 28.55, 27.68 and 24.63 Gy for C-Loop, bowel and stomach, respectively. Using D0.03cc as max dose surrogate, the value was 28.03, 27.12 and 23.84 Gy for C-Loop, bowel and stomach, respectively. Max dose constraint of 29 Gy was achieved for 81.3% cases for C-Loop and stomach, and 78.1% for bowel. Using D0.03cc as max dose surrogate, the passing rate was 90.6% for C-Loop, and 81.3% for bowel and stomach. Manhattan plot revealed high correlation between the OAR over dose and the minimal distance between the PTV33 and OAR. Conclusions: The results showed promising robustness of the pancreas SBRT AI tool, providing important evidence of its readiness for clinical implementation. The established workflow could guide the process of assuring clinical readiness of future AI based treatment planning tools.

5.
Quant Imaging Med Surg ; 11(12): 4859-4880, 2021 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-34888195

RESUMO

Artificial intelligence (AI) refers to methods that improve and automate challenging human tasks by systematically capturing and applying relevant knowledge in these tasks. Over the past decades, a number of approaches have been developed to address different types and needs of system intelligence ranging from search strategies to knowledge representation and inference to robotic planning. In the context of radiation treatment planning, multiple AI approaches may be adopted to improve the planning quality and efficiency. For example, knowledge representation and inference methods may improve dose prescription by integrating and reasoning about the domain knowledge described in many clinical guidelines and clinical trials reports. In this review, we will focus on the most studied AI approach in intensity modulated radiation therapy (IMRT)/volumetric modulated arc therapy (VMAT)-machine learning (ML) and describe our recent efforts in applying ML to improve the quality, consistency, and efficiency of IMRT/VMAT planning. With the available high-quality data, we can build models to accurately predict critical variables for each step of the planning process and thus automate and improve its outcomes. Specific to the IMRT/VMAT planning process, we can build models for each of the four critical components in the process: dose-volume histogram (DVH), Dose, Fluence, and Human Planner. These models can be divided into two general groups. The first group focuses on encoding prior experience and knowledge through ML and more recently deep learning (DL) from prior clinical plans and using these models to predict the optimal DVH (DVH prediction model), or 3D dose distribution (dose prediction model), or fluence map (fluence map model). The goal of these models is to reduce or remove the trial-and-error process and guarantee consistently high-quality plans. The second group of models focuses on mimicking human planners' decision-making process (planning strategy model) during the iterative adjustments/guidance of the optimization engine. Each critical step of the IMRT/VMAT treatment planning process can be improved and automated by AI methods. As more training data becomes available and more sophisticated models are developed, we can expect that the AI methods in treatment planning will continue to improve accuracy, efficiency, and robustness.

6.
Adv Radiat Oncol ; 6(6): 100760, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34934856

RESUMO

Purpose: To examine the effectiveness and safety of single-isocenter multitarget stereotactic radiosurgery using a volume-adapted dosing strategy in patients with 4 to 10 brain metastases. Methods and Materials: Adult patients with 4 to 10 brain metastases were eligible for this prospective trial. The primary endpoint was overall survival. Secondary endpoints were local recurrence, distant brain failure, neurologic death, and rate of adverse events. Exploratory objectives were neurocognition, quality of life, dosimetric data, salvage rate, and radionecrosis. Dose was prescribed in a single fraction per RTOG 90-05 or as 5 Gy × 5 fractions for lesions ≥3 cm diameter, lesions involving critical structures, or single-fraction brain V12Gy >20 mL. Results: Forty patients were treated with median age of 61 years, Karnofsky performance status 90, and 6 brain metastases. Twenty-two patients survived longer than expected from the time of protocol SRS, with 1 living patient who has not reached that milestone. Median overall survival was 8.1 months with a 1-year overall survival of 35.7%. The 1-year local recurrence rate was 5% (10 of 204 of evaluable lesions) in 12.5% (4 of 32) of the patients. Distant brain failure was observed in 19 of 32 patients with a 1-year rate of 35.8%. Grade 1-2 headache was the most common complaint, with no grade 3-5 treatment-related adverse events. Radionecrosis was observed in only 5 lesions, with a 1-year rate of 1.5%. Rate of neurologic death was 20%. Neurocognition and quality of life did not significantly change 3 months after SRS compared with pretreatment. Conclusions: These results suggest that volume-adapted dosing single-isocenter multitarget stereotactic radiosurgery is an effective and safe treatment for patients with 4 to 10 brain metastases.

7.
Phys Med Biol ; 66(24)2021 Dec 06.
Artigo em Inglês | MEDLINE | ID: mdl-34808605

RESUMO

Objective:To design a deep transfer learning framework for modeling fluence map predictions for stereotactic body radiation therapy (SBRT) of adrenal cancer and similar sites that usually have a small number of cases.Approach:We developed a transfer learning framework for adrenal SBRT planning that leverages knowledge in a pancreas SBRT planning model. Treatment plans from the two sites had different dose prescriptions and beam settings but both prioritized gastrointestinal sparing. A base framework was first trained with 100 pancreas cases. This framework consists of two convolutional neural networks (CNN), which predict individual beam doses (BD-CNN) and fluence maps (FM-CNN) sequentially for 9-beam intensity-modulated radiation therapy (IMRT) plans. Forty-five adrenal plans were split into training/validation/test sets with the ratio of 20/10/15. The base BD-CNN was re-trained with transfer learning using 5/10/15/20 adrenal training cases to produce multiple candidate adrenal BD-CNN models. The base FM-CNN was directly used for adrenal cases. The deep learning (DL) plans were evaluated by several clinically relevant dosimetric endpoints, producing a percentage score relative to the clinical plans.Main results:Transfer learning significantly reduced the number of training cases and training time needed to train such a DL framework. The adrenal transfer learning model trained with 5/10/15/20 cases achieved validation plan scores of 85.4/91.2/90.7/89.4, suggesting that model performance saturated with 10 training cases. Meanwhile, a model using all 20 adrenal training cases without transfer learning only scored 80.5. For the final test set, the 5/10/15/20-case models achieved scores of 73.5/75.3/78.9/83.3.Significance:It is feasible to use deep transfer learning to train an IMRT fluence prediction framework. This technique could adapt to different dose prescriptions and beam configurations. This framework potentially enables DL modeling for clinical sites that have a limited dataset, either due to few cases or due to rapid technology evolution.

8.
Phys Med Biol ; 66(23)2021 Nov 26.
Artigo em Inglês | MEDLINE | ID: mdl-34757945

RESUMO

Purpose.We have previously reported an artificial intelligence (AI) agent that automatically generates intensity-modulated radiation therapy (IMRT) plans via fluence map prediction, by-passing inverse planning. This AI agent achieved clinically comparable quality for prostate cases, but its performance on head-and-neck patients leaves room for improvement. This study aims to collect insights of the deep-learning-based (DL-based) fluence map prediction model by systematically analyzing its prediction errors.Methods.From the modeling perspective, the DL model's output is the fluence maps of IMRT plans. However, from the clinical planning perspective, the plan quality evaluation should be based on the clinical dosimetric criteria such as dose-volume histograms. To account for the complex and non-intuitive relationships between fluence map prediction errors and the corresponding dose distribution changes, we propose a novel error analysis approach that systematically examines plan dosimetric changes that are induced by varying amounts of fluence prediction errors. We investigated four decomposition modes of model prediction errors. The two spatial domain decompositions are based on fluence intensity and fluence gradient. The two frequency domain decompositions are based on Fourier-space banded frequency rings and Fourier-space truncated low-frequency disks. The decomposed error was analyzed for its impact on the resulting plans' dosimetric metrics. The analysis was conducted on 15 test cases spared from the 200 training and 16 validation cases used to train the model.Results.Most planning target volume metrics were significantly correlated with most error decompositions. The Fourier space disk radii had the largest Spearman's coefficients. The low-frequency region within a disk of ∼20% Fourier space contained most of errors that impact overall plan quality.Conclusions.This study demonstrates the feasibility of using fluence map prediction error analysis to understand the AI agent's performance. Such insights will help fine-tune the DL models in architecture design and loss function selection.

9.
J Agric Food Chem ; 69(41): 12156-12170, 2021 Oct 20.
Artigo em Inglês | MEDLINE | ID: mdl-34623798

RESUMO

Enlightened from our previous work of structural simplification of quinine and innovative application of natural products against phytopathogenic fungi, lead structure 2,8-bis(trifluoromethyl)-4-quinolinol (3) was selected to be a candidate and its diversified design, synthesis, and antifungal evaluation were carried out. All of the synthesized compounds Aa1-Db1 were evaluated for their antifungal activity against four agriculturally important fungi, Botrytis cinerea, Fusarium graminearum, Rhizoctonia solani, and Sclerotinia sclerotiorum. Results showed that compounds Ac3, Ac4, Ac7, Ac9, Ac12, Bb1, Bb10, Bb11, Bb13, Cb1. and Cb3 exhibited a good antifungal effect, especially Ac12 had the most potent activity with EC50 values of 0.52 and 0.50 µg/mL against S. sclerotiorum and B. cinerea, respectively, which were more potent than those of the lead compound 3 (1.72 and 1.89 µg/mL) and commercial fungicides azoxystrobin (both >30 µg/mL) and 8-hydroxyquinoline (2.12 and 5.28 µg/mL). Moreover, compound Ac12 displayed excellent in vivo antifungal activity, which was comparable in activity to the commercial fungicide boscalid. The preliminary mechanism revealed that compound Ac12 might cause an abnormal morphology of cell membranes, an increase in membrane permeability, and release of cellular contents. These results indicated that compound Ac12 displayed superior in vitro and in vivo fungicidal activities and could be a potential fungicidal candidate against plant fungal diseases.


Assuntos
Fungicidas Industriais , Fusarium , Hidroxiquinolinas , Quinolinas , Antifúngicos/farmacologia , Ascomicetos , Botrytis , Fungos , Fungicidas Industriais/farmacologia , Estrutura Molecular , Quinina , Rhizoctonia , Relação Estrutura-Atividade
10.
Int J Mol Sci ; 22(19)2021 Oct 07.
Artigo em Inglês | MEDLINE | ID: mdl-34639194

RESUMO

Humulus lupulus Linn. is a traditional medicinal and edible plant with several biological properties. The aims of this work were: (1) to evaluate the in vitro antifungal activity of H. lupulus ethanolic extract; (2) to study the in vitro and in vivo antifungal activity of isoxanthohumol, an isoprene flavonoid from H. lupulus, against Botrytis cinerea; and (3) to explore the antifungal mechanism of isoxanthohumol on B. cinerea. The present data revealed that the ethanolic extract of H. lupulus exhibited moderate antifungal activity against the five tested phytopathogenic fungi in vitro, and isoxanthohumol showed highly significant antifungal activity against B. cinerea, with an EC50 value of 4.32 µg/mL. Meanwhile, it exhibited moderate to excellent protective and curative efficacies in vivo. The results of morphologic observation, RNA-seq, and physiological indicators revealed that the antifungal mechanism of isoxanthohumol is mainly related to metabolism; it affected the carbohydrate metabolic process, destroyed the tricarboxylic acid (TCA) cycle, and hindered the generation of ATP by inhibiting respiration. Further studies indicated that isoxanthohumol caused membrane lipid peroxidation, thus accelerating the death of B. cinerea. This study demonstrates that isoxanthohumol can be used as a potential botanical fungicide for the management of phytopathogenic fungi.


Assuntos
Trifosfato de Adenosina/metabolismo , Antifúngicos/farmacologia , Botrytis/efeitos dos fármacos , Humulus/química , Peroxidação de Lipídeos/efeitos dos fármacos , Xantonas/farmacologia , Botrytis/crescimento & desenvolvimento
11.
Med Phys ; 2021 Oct 27.
Artigo em Inglês | MEDLINE | ID: mdl-34706072

RESUMO

PURPOSE: To develop a novel multi-contrast four-dimensional magnetic resonance imaging (MC-4D-MRI) technique that expands single image contrast 4D-MRI to a spectrum of native and synthetic image contrasts and to evaluate its feasibility in liver tumor patients. METHODS AND MATERIALS: The MC-4D-MRI technique integrates multi-parametric MRI fusion, 4D-MRI, and deformable image registration (DIR) techniques. The fusion technique consists of native MRI as input, image pre-processing, fusion algorithm, adaptation, and fused multi-contrast MRI as output. Four-dimensional deformation vector fields (4D-DVF) were generated from an original T2/T1-w 4D-MRI by deforming end-of-inhalation (EOI) to nine other phase volumes via DIR. The 4D-DVF were applied to multi-contrast MRI to generate a spectrum of 4D-MRI in different image contrasts. The MC-4D-MRI technique was evaluated in five liver tumor patients on tumor contrast-to-noise ratio (CNR), internal target volume (ITV) contouring consistency, diaphragm motion range, and tumor motion trajectory; and in digital anthropomorphic phantoms on 4D-DIR introduced errors in tumor motion range, centroid location, extent, and volume. RESULTS: MC-4D-MRI consisting of 4D-MRIs in native image contrasts (T1-w, T2-w, and T2/T1-w) and synthetic image contrasts, such as tumor-enhanced contrast (TEC) were generated in five liver tumor patients. Patient tumor CNR increased from 2.6 ± 1.8 in the T2/T1-w MRI, to -4.4 ± 2.4, 6.6 ± 3.0, and 9.6 ± 3.9 in the T1-w, T2-w, and TEC MRI, respectively. Patient ITV inter-observer mean Dice similarity coefficient (mDSC) increased from 0.65 ± 0.10 in the original T2/T1-w 4D-MRI, to 0.76 ± 0.14, 0.77 ± 0.12, and 0.86 ± 0.05 in the T1-w, T2-w, and TEC 4D-MRI, respectively. Patient diaphragm motion range absolute differences between the three new 4D-MRIs and original T2/T1-w 4D-MRI were 1.2 ± 1.3, 0.3 ± 0.7, and 0.5 ± 0.5 mm, respectively. Patient tumor displacement phase-averaged absolute differences between the three 4D-MRIs and the original 4D-MRI were 0.72 ± 0.33, 0.62 ± 0.54, and 0.74 ± 0.43 mm in the superior-inferior (SI) direction, and 0.59 ± 0.36, 0.51 ± 0.30, and 0.50 ± 0.24 mm in the anterior-posterior (AP) direction, respectively. In the digital phantoms, phase-averaged absolute tumor centroid shift caused by the 4D-DIR were at or below 0.5 mm in SI, AP, and left-right (LR) directions. CONCLUSION: We developed an MC-4D-MRI technique capable of expanding single image contrast 4D-MRI along a new dimension of image contrast. Initial evaluations in liver tumor patients showed enhancements in image contrast variety, tumor contrast, and ITV contouring consistencies using MC-4D-MRI. The technique might offer new perspectives on the image contrast of MRI and 4D-MRI in MR-guided radiotherapy.

12.
Chemosphere ; : 132638, 2021 Oct 20.
Artigo em Inglês | MEDLINE | ID: mdl-34687678

RESUMO

A polytetrafluoroethylene (PTFE) doped PbO2 anode with a highly hydrophobicity was fabricated by electrodeposition method. In this process, vertically aligned TiO2 nanotubes (TiO2NTs) are formed by the anodic oxidation of Ti plates as an intermediate layer for PbO2 electrodeposition. The characterization of the electrodes indicated that PTFE was successfully introduced to the electrode surface, the TiO2NTs were completely covered with ß-PbO2 particles and gave it a large surface area, which also limited the growth of its crystal particles. Compared with the conventional Ti/PbO2 and Ti/TiO2NTs/PbO2 electrode, the Ti/TiO2NTs/PbO2-PTFE electrode has enhanced surface hydrophobicity, higher oxygen evolution potential, lower electrochemical impedance, with more active sites, and generate more hydroxyl radicals (·OH), which were enhanced by the addition of PTFE nanoparticles. The electrocatalytic performance of the three electrodes were investigated using dibutyl phthalate (DBP) as the model pollutant. The efficiency of the DBP removal of the three electrodes was in the order: Ti/TiO2NTs/PbO2-PTFE > Ti/TiO2NTs/PbO2 > Ti/PbO2. The degradation process followed the pseudo-first-order kinetic model well, with rate constants of 0.1326, 0.1266, and 0.1041 h-1 for the three electrodes, respectively. The lowest energy consumption (6.1 kWh g-1) was obtained after 8 h of DBP treatment using Ti/TiO2NTs/PbO2-PTFE compared to Ti/TiO2NTs/PbO2 (6.7 kWh g-1) and Ti/PbO2 (7.4 kWh g-1) electrodes. Moreover, the effects of current density, initial pH and electrolyte concentration were investigated. Finally, the products of the DBP degradation process were verified based on gas chromatography-mass spectrometry analysis, and possible degradation pathways were described.

13.
J Agric Food Chem ; 69(40): 11781-11793, 2021 Oct 13.
Artigo em Inglês | MEDLINE | ID: mdl-34582205

RESUMO

Plant pathogenic fungi seriously affect agricultural production and are difficult to control. The discovery of new leads based on natural products is an important way to innovate fungicides. In this study, 30 natural-product-based magnolol derivatives were synthesized and characterized on the basis of NMR and mass spectroscopy. Bioactivity tests on phytopathogenic fungi (Rhizoctonia solani, Fusarium graminearum, Botrytis cinerea, and Sclerotinia sclerotiorum) in vitro of these compounds were performed systematically. The results showed that 11 compounds were active against four kinds of phytopathogenic fungi with EC50 values in the range of 1.40-20.00 µg/mL, especially compound L5 that exhibited excellent antifungal properties against B. cinerea with an EC50 value of 2.86 µg/mL, approximately 2.8-fold more potent than magnolol (EC50 = 8.13 µg/mL). Moreover, compound L6 showed the highest antifungal activity against F. graminearum and Rhophitulus solani with EC50 values of 4.39 and 1.40 µg/mL, respectively, and compound L7 showed good antifungal activity against S. sclerotiorum. Then, an in vivo experiment of compound L5 against B. cinerea was further investigated in vivo using infected tomatoes (curative effect, 50/200 and 36%/100 µg/mL). The physiological and biochemical studies illustrated that the primary action mechanism of compound L5 on B. cinerea might change the mycelium morphology, increase cell membrane permeability, and destroy the function of mitochondria. Furthermore, structure-activity relationship (SAR) studies revealed that hydroxyl groups play a key role in antifungal activity. To sum up, this study provides a reference for understanding the application of magnolol-based antifungal agents in crop protection.


Assuntos
Antifúngicos , Fungicidas Industriais , Animais , Antifúngicos/farmacologia , Ascomicetos , Compostos de Bifenilo , Botrytis , Fungicidas Industriais/farmacologia , Fusarium , Lignanas , Estrutura Molecular , Rhizoctonia , Relação Estrutura-Atividade
14.
Clin Appl Thromb Hemost ; 27: 1076029620967108, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34583575

RESUMO

To explore the possible single nucleotide polymorphisms (SNPs) sites in the promoter region of fibrinogen B ß (FGB), and construct logistic regression model and haplotype model, so as to reveal the influence of FGB promoter SNPs on susceptibility, hemodynamics and coagulation function of lower extremity deep venous thrombosis (LEDVT) in the genetic background. LEDVT patients (120) and healthy people (120) were taken as case and control objects, respectively. SNPs and their genotypes of FGB promoter were detected by promoter sequencing and PCR-RFLP. The parameters of coagulation system were evaluated. There were 6 SNPs in FGB promoter, which were ß-148C/T, ß-249C/T, ß-455G/A, ß-854G/A, ß-993C/T and ß-1420G/A. The genotype and allele frequency of ß-1420 G/A, ß-455G/A, ß-249c/T and ß-148C/T were significantly different between the LEDVT group and the control group, but not ß-993C/T and ß-854G/A. In addition, we found that the higher the content of Fibrinogen (FG), the higher the risk of LEDVT. The risk of LEDVT increased by 4.579 times for every unit increase of fibrinogen. We also found that FG, PT and APTT in LEDVT group were higher than those in control group, while TT was lower than those in control group; Furthermore, there was no significant difference in all coagulation indexes among 6 SNP genotypes in LEDVT group, while a significant difference was found between the 2 genotypes of ß-993C/T in the control group. ß-993C/T may indirectly affect the susceptibility of LEDVT by improving the basic level of plasma FG.

15.
J Agric Food Chem ; 69(38): 11395-11405, 2021 Sep 29.
Artigo em Inglês | MEDLINE | ID: mdl-34523907

RESUMO

Plant pathogenic fungi seriously threaten agricultural production. There is an urgent need to develop novel fungicides with low toxicity and high efficiency. In this study, we designed and synthesized 44 pyrazolo[3,4-d]pyrimidin-4-one derivatives and evaluated them for their fungicidal activities. The bioassay data revealed that most of the target compounds possessed moderate to high in vitro antifungal activities. Especially compound g22 exhibited remarkable antifungal activity against Sclerotinia sclerotiorum with an EC50 value of 1.25 mg/L, close to that of commercial fungicide boscalid (EC50 = 0.96 mg/L) and fluopyram (EC50 = 1.91 mg/L). Moreover, compound g22 possessed prominent protective activity against S. sclerotiorum in vivo for 24 h (95.23%) and 48 h (93.78%), comparable to positive control boscalid (24 h (96.63%); 48 h (93.23%)). Subsequent studies indicated that compound g22 may impede the growth and reproduction of S. sclerotiorum by affecting the morphology of mycelium, destroying cell membrane integrity, and increasing cell membrane permeability. In addition, the application of compound g22 did not injure the growth or reproduction of Italian bees. This study revealed that compound g22 is expected to be developed for efficient and safe agricultural fungicides.


Assuntos
Ascomicetos , Fungicidas Industriais , Animais , Antifúngicos/farmacologia , Fungicidas Industriais/farmacologia , Relação Estrutura-Atividade
16.
J Appl Clin Med Phys ; 22(10): 329-337, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34432946

RESUMO

BACKGROUND AND PURPOSE: The efficacy of clinical trials and the outcome of patient treatment are dependent on the quality assurance (QA) of radiation therapy (RT) plans. There are two widely utilized approaches that include plan optimization guidance created based on patient-specific anatomy. This study examined these two techniques for dose-volume histogram predictions, RT plan optimizations, and prospective QA processes, namely the knowledge-based planning (KBP) technique and another first principle (FP) technique. METHODS: This analysis included 60, 44, and 10 RT plans from three Radiation Therapy Oncology Group (RTOG) multi-institutional trials: RTOG 0631 (Spine SRS), RTOG 1308 (NSCLC), and RTOG 0522 (H&N), respectively. Both approaches were compared in terms of dose prediction and plan optimization. The dose predictions were also compared to the original plan submitted to the trials for the QA procedure. RESULTS: For the RTOG 0631 (Spine SRS) and RTOG 0522 (H&N) plans, the dose predictions from both techniques have correlation coefficients of >0.9. The RT plans that were re-optimized based on the predictions from both techniques showed similar quality, with no statistically significant differences in target coverage or organ-at-risk sparing. The predictions of mean lung and heart doses from both methods for RTOG1308 patients, on the other hand, have a discrepancy of up to 14 Gy. CONCLUSIONS: Both methods are valuable tools for optimization guidance of RT plans for Spine SRS and Head and Neck cases, as well as for QA purposes. On the other hand, the findings suggest that KBP may be more feasible in the case of inoperable lung cancer patients who are treated with IMRT plans that have spatially unevenly distributed beam angles.


Assuntos
Neoplasias Pulmonares , Radioterapia de Intensidade Modulada , Humanos , Órgãos em Risco , Estudos Prospectivos , Dosagem Radioterapêutica , Planejamento da Radioterapia Assistida por Computador
17.
Abdom Radiol (NY) ; 2021 Aug 25.
Artigo em Inglês | MEDLINE | ID: mdl-34435228

RESUMO

Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.

18.
Bioresour Technol ; 340: 125619, 2021 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-34325391

RESUMO

Microbial electrolysis cell coupled anaerobic digestion (MEC-AD) is a new technology in energy recovery and waste treatment, which could be used to recycle swine manure. Here, different applied voltage effects were studied using MEC-AD with swine manure as a substrate. The maximum cumulative biogas and methane yields, both occurring with 0.9 V, were 547.3 mL/g total solid (TS) and 347.7 mL/g TS, respectively. The increased energy can counterbalance the electrical input. First order, logistic, gompertz, and back-propagation artificial neural network (BP-ANN) models were used to study cumulative biogas and methane yields. The BP-ANN model was superior to the other three models. The maximum degradation rate of hemicellulose, cellulose, and lignin was 60.97%, 48.59%, and 31.59% at 0.9 V, respectively. The BP-ANN model establishes a model for cumulative biogas and methane yields using MEC-AD. Thus, MEC-AD enhanced biogas and methane production and accelerated substrate degradation at a suitable voltage.


Assuntos
Reatores Biológicos , Esterco , Anaerobiose , Animais , Biocombustíveis , Eletrólise , Metano , Suínos
19.
Phys Med Biol ; 66(15)2021 Jul 30.
Artigo em Inglês | MEDLINE | ID: mdl-34261057

RESUMO

Purpose.Although deep learning (DL) technique has been successfully used for computed tomography (CT) reconstruction, its implementation on cone-beam CT (CBCT) reconstruction is extremely challenging due to memory limitations. In this study, a novel DL technique is developed to resolve the memory issue, and its feasibility is demonstrated for CBCT reconstruction from sparsely sampled projection data.Methods.The novel geometry-guided deep learning (GDL) technique is composed of a GDL reconstruction module and a post-processing module. The GDL reconstruction module learns and performs projection-to-image domain transformation by replacing the traditional single fully connected layer with an array of small fully connected layers in the network architecture based on the projection geometry. The DL post-processing module further improves image quality after reconstruction. We demonstrated the feasibility and advantage of the model by comparing ground truth CBCT with CBCT images reconstructed using (1) GDL reconstruction module only, (2) GDL reconstruction module with DL post-processing module, (3) Feldkamp, Davis, and Kress (FDK) only, (4) FDK with DL post-processing module, (5) ray-tracing only, and (6) ray-tracing with DL post-processing module. The differences are quantified by peak-signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root-mean-square error (RMSE).Results.CBCT images reconstructed with GDL show improvements in quantitative scores of PSNR, SSIM, and RMSE. Reconstruction time per image for all reconstruction methods are comparable. Compared to current DL methods using large fully connected layers, the estimated memory requirement using GDL is four orders of magnitude less, making DL CBCT reconstruction feasible.Conclusion.With much lower memory requirement compared to other existing networks, the GDL technique is demonstrated to be the first DL technique that can rapidly and accurately reconstruct CBCT images from sparsely sampled data.

20.
Phys Med Biol ; 66(11)2021 05 31.
Artigo em Inglês | MEDLINE | ID: mdl-34061044

RESUMO

Objective. Synthesize realistic and controllable respiratory motions in the extended cardiac-torso (XCAT) phantoms by developing a generative adversarial network (GAN)-based deep learning technique.Methods. A motion generation model was developed using bicycle-GAN with a novel 4D generator. Input with the end-of-inhale (EOI) phase images and a Gaussian perturbation, the model generates inter-phase deformable-vector-fields (DVFs), which were composed and applied to the input to generate 4D images. The model was trained and validated using 71 4D-CT images from lung cancer patients and then applied to the XCAT EOI images to generate 4D-XCAT with realistic respiratory motions. A separate respiratory motion amplitude control model was built using decision tree regression to predict the input perturbation needed for a specific motion amplitude, and this model was developed using 300 4D-XCAT generated from 6 XCAT phantom sizes with 50 different perturbations for each size. In both patient and phantom studies, Dice coefficients for lungs and lung volume variation during respiration were compared between the simulated images and reference images. The generated DVF was evaluated by deformation energy. DVFs and ventilation maps of the simulated 4D-CT were compared with the reference 4D-CTs using cross correlation and Spearman's correlation. Comparison of DVFs and ventilation maps among the original 4D-XCAT, the generated 4D-XCAT, and reference patient 4D-CTs were made to show the improvement of motion realism by the model. The amplitude control error was calculated.Results. Comparing the simulated and reference 4D-CTs, the maximum deviation of lung volume during respiration was 5.8%, and the Dice coefficient reached at least 0.95 for lungs. The generated DVFs presented comparable deformation energy levels. The cross correlation of DVFs achieved 0.89 ± 0.10/0.86 ± 0.12/0.95 ± 0.04 along thex/y/zdirection in the testing group. The cross correlation of ventilation maps derived achieved 0.80 ± 0.05/0.67 ± 0.09/0.68 ± 0.13, and the Spearman's correlation achieved 0.70 ± 0.05/0, 60 ± 0.09/0.53 ± 0.01, respectively, in the training/validation/testing groups. The generated 4D-XCAT phantoms presented similar deformation energy as patient data while maintained the lung volumes of the original XCAT phantom (Dice = 0.95, maximum lung volume variation = 4%). The motion amplitude control models controlled the motion amplitude control error to be less than 0.5 mm.Conclusions. The results demonstrated the feasibility of synthesizing realistic controllable respiratory motion in the XCAT phantom using the proposed method. This crucial development enhances the value of XCAT phantoms for various 4D imaging and therapy studies.


Assuntos
Tomografia Computadorizada Quadridimensional , Respiração , Humanos , Movimento (Física) , Imagens de Fantasmas , Tronco
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