Design, synthesis and biological evaluation of organophosphorous-homodimers as dual binding site acetylcholinesterase inhibitors.

The cluster effect is an effective strategy to explore new lead compounds, and has been successfully applied in rational drug design and screening. A series of novel organophosphorous-homodimers were designed and synthesized based on the dual-site structure characteristics of acetylcholinesterase (AChE). The compounds were evaluated in vitro for their inhibitory activity to AChE extracted from Drosophila melanogaster and Musca domestic. Compound 4H showed an excellent inhibitor activity to both Drosophila melanogaster and Musca domestic with the corresponding IC(50) values of 23 and 168 nM, respectively. Meanwhile, its activities against Drosophila melanogaster and Musca domestic AChE were more than 10,00,000 and 100,000-fold higher compared with the parent compound (MH), and was up to 245 and 107-fold higher than those of the positive control omethoate. The molecular docking study revealed that 4H possessed an optimal spacer length and can perfectly fit into the central pocket, active gorge, and peripheral site of DmAChE, and consequently exhibited highly improved inhibitor potency to DmAChE. The bioassay tests showed that 4 series compounds showed prominent insecticidal activities against both Lipaphser erysimi and Tetranychus cinnbarinus at a concentration of 200mg/L. The insecticide activity of compound 4H was particularly significant that can cause 96% mortality to Tetranychus cinnbarinus after 24h of treatment.

Segmentation of multiple Organs-at-Risk associated with brain tumors based on coarse-to-fine stratified networks.

BACKGROUND: Delineation of Organs-at-Risks (OARs) is an important step in radiotherapy treatment planning. As manual delineation is time-consuming, labor-intensive and affected by inter- and intra-observer variability, a robust and efficient automatic segmentation algorithm is highly desirable for improving the efficiency and repeatability of OAR delineation. PURPOSE: Automatic segmentation of OARs in medical images is challenged by low contrast, various shapes and imbalanced sizes of different organs. We aim to overcome these challenges and develop a high-performance method for automatic segmentation of 10 OARs required in radiotherapy planning for brain tumors. METHODS: A novel two-stage segmentation framework is proposed, where a coarse and simultaneous localization of all the target organs is obtained in the first stage, and a fine segmentation is achieved for each organ, respectively, in the second stage. To deal with organs with various sizes and shapes, a stratified segmentation strategy is proposed, where a High- and Low-Resolution Residual Network (HLRNet) that consists of a multiresolution branch and a high-resolution branch is introduced to segment medium-sized organs, and a High-Resolution Residual Network (HRRNet) is used to segment small organs. In addition, a label fusion strategy is proposed to better deal with symmetric pairs of organs like the left and right cochleas and lacrimal glands. RESULTS: Our method was validated on the dataset of MICCAI ABCs 2020 challenge for OAR segmentation. It obtained an average Dice of 75.8% for 10 OARs, and significantly outperformed several state-of-the-art models including nnU-Net (71.6%) and FocusNet (72.4%). Our proposed HLRNet and HRRNet improved the segmentation accuracy for medium-sized and small organs, respectively. The label fusion strategy led to higher accuracy for symmetric pairs of organs. CONCLUSIONS: Our proposed method is effective for the segmentation of OARs of brain tumors, with a better performance than existing methods, especially on medium-sized and small organs. It has a potential for improving the efficiency of radiotherapy planning with high segmentation accuracy.

Efficient Multi-Organ Segmentation From 3D Abdominal CT Images With Lightweight Network and Knowledge Distillation.

Accurate segmentation of multiple abdominal organs from Computed Tomography (CT) images plays an important role in computer-aided diagnosis, treatment planning and follow-up. Currently, 3D Convolution Neural Networks (CNN) have achieved promising performance for automatic medical image segmentation tasks. However, most existing 3D CNNs have a large set of parameters and huge floating point operations (FLOPs), and 3D CT volumes have a large size, leading to high computational cost, which limits their clinical application. To tackle this issue, we propose a novel framework based on lightweight network and Knowledge Distillation (KD) for delineating multiple organs from 3D CT volumes. We first propose a novel lightweight medical image segmentation network named LCOV-Net for reducing the model size and then introduce two knowledge distillation modules (i.e., Class-Affinity KD and Multi-Scale KD) to effectively distill the knowledge from a heavy-weight teacher model to improve LCOV-Net's segmentation accuracy. Experiments on two public abdominal CT datasets for multiple organ segmentation showed that: 1) Our LCOV-Net outperformed existing lightweight 3D segmentation models in both computational cost and accuracy; 2) The proposed KD strategy effectively improved the performance of the lightweight network, and it outperformed existing KD methods; 3) Combining the proposed LCOV-Net and KD strategy, our framework achieved better performance than the state-of-the-art 3D nnU-Net with only one-fifth parameters. The code is available at https://github.com/HiLab-git/LCOVNet-and-KD.

PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation.

BACKGROUND AND OBJECTIVE: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models that are essential for computer-assisted diagnosis and treatment procedures. Existing toolkits mainly focus on fully supervised segmentation that assumes full and accurate pixel-level annotations are available. Such annotations are time-consuming and difficult to acquire for segmentation tasks, which makes learning from imperfect labels highly desired for reducing the annotation cost. We aim to develop a new deep learning toolkit to support annotation-efficient learning for medical image segmentation, which can accelerate and simplify the development of deep learning models with limited annotation budget, e.g., learning from partial, sparse or noisy annotations. METHODS: Our proposed toolkit named PyMIC is a modular deep learning library for medical image segmentation tasks. In addition to basic components that support development of high-performance models for fully supervised segmentation, it contains several advanced components that are tailored for learning from imperfect annotations, such as loading annotated and unannounced images, loss functions for unannotated, partially or inaccurately annotated images, and training procedures for co-learning between multiple networks, etc. PyMIC is built on the PyTorch framework and supports development of semi-supervised, weakly supervised and noise-robust learning methods for medical image segmentation. RESULTS: We present several illustrative medical image segmentation tasks based on PyMIC: (1) Achieving competitive performance on fully supervised learning; (2) Semi-supervised cardiac structure segmentation with only 10% training images annotated; (3) Weakly supervised segmentation using scribble annotations; and (4) Learning from noisy labels for chest radiograph segmentation. CONCLUSIONS: The PyMIC toolkit is easy to use and facilitates efficient development of medical image segmentation models with imperfect annotations. It is modular and flexible, which enables researchers to develop high-performance models with low annotation cost. The source code is available at:https://github.com/HiLab-git/PyMIC.

HMRNet: High and Multi-Resolution Network With Bidirectional Feature Calibration for Brain Structure Segmentation in Radiotherapy.

Accurate segmentation of Anatomical brain Barriers to Cancer spread (ABCs) plays an important role for automatic delineation of Clinical Target Volume (CTV) of brain tumors in radiotherapy. Despite that variants of U-Net are state-of-the-art segmentation models, they have limited performance when dealing with ABCs structures with various shapes and sizes, especially thin structures (e.g., the falx cerebri) that span only few slices. To deal with this problem, we propose a High and Multi-Resolution Network (HMRNet) that consists of a multi-scale feature learning branch and a high-resolution branch, which can maintain the high-resolution contextual information and extract more robust representations of anatomical structures with various scales. We further design a Bidirectional Feature Calibration (BFC) block to enable the two branches to generate spatial attention maps for mutual feature calibration. Considering the different sizes and positions of ABCs structures, our network was applied after a rough localization of each structure to obtain fine segmentation results. Experiments on the MICCAI 2020 ABCs challenge dataset showed that: 1) Our proposed two-stage segmentation strategy largely outperformed methods segmenting all the structures in just one stage; 2) The proposed HMRNet with two branches can maintain high-resolution representations and is effective to improve the performance on thin structures; 3) The proposed BFC block outperformed existing attention methods using monodirectional feature calibration. Our method won the second place of ABCs 2020 challenge and has a potential for more accurate and reasonable delineation of CTV of brain tumors.

Homo- and hetero-dimers of inactive organophosphorous group binding at dual sites of AChE.

Homo- and hetero-dimers of inactive organophosphorous group(s) dramatically enhanced the acetylcholinesterase (AChE; EC 3.1.1.7) inhibiting potency, with the highest potency observed at a tether length of 6 methylene groups (6d) for the homodimers, and 7 methylene groups (8e) for the heterodimers. The docking model of Drosophila melanogaster AChE suggested that 6d and 8e bound at the catalytic and peripheral sites of AChE, in which two organophosphorous groups of 6d individually oriented towards TRP83 of catalytic sites and TRP321 of peripheral sites, and phthalicimide group of 8e was appropriately arranged for a π-π interaction with the phenyl ring of TYR330, furthermore, the organophosphorous group introduced hydrophobic interaction with TRP83. The compounds prepared in this work demonstrated high insecticidal activity to Lipaphis erysimi and Tetranychus cinnbarinus at the concentration 300mg/L.

4-Chloro-5-[(5,5-dimethyl-4,5-dihydro-isoxazol-3-yl)sulfonyl-meth-yl]-3-methyl-1-(2,2,2-trifluoro-ethyl)-1H-pyrazole.

The mol-ecule of the title compound, C(12)H(15)ClF(3)N(3)O(3)S, is twisted, as indicated by the C-S-C-C torsion angle of 66.00 (18)° for the atoms linking the ring systems. An intra-molecular C-H⋯F short contact occurs. In the crystal, non-classical C-H⋯O inter-actions, one of which has a short H⋯O contact of 2.28 Å, link the mol-ecules.

4-Bromo-5-[(5,5-dimethyl-4,5-dihydro-isoxazol-3-yl)sulfonyl-meth-yl]-3-methyl-1-(2,2,2-trifluoro-ethyl)-1H-pyrazole.

In the title compound, C(12)H(15)BrF(3)N(3)O(3)S, which has potential herbicidal activity, the mol-ecule is twisted, as indicated by the C-S-C-C torsion angle of 67.86 (19)° for the atoms linking the ring systems. An intra-molecular C-H⋯F short contact occurs and inter-molecular C-H⋯O inter-actions link the mol-ecules in the crystal.

Leucocytes DNA damage in mice exposed to JS-118 by the comet assay.

JS-118 is an extensively used insecticide in China. The present study investigated the genotoxic effect of JS-118 on whole blood at 24, 48, 72 and 96 h by using alkaline comet assay. Male Kunming mice were given 6.25, 12.5, 25, 50 and 100 mg/kg BW of JS-118 intraperitoneally. A statistically significant increase in all comet parameters indicating DNA damage was observed at 24 h post-treatment (p < 0.05). A clear concentration-dependent increase of DNA damage was revealed as evident by the OTM (arbitrary units), tail length (µm) and tail DNA (%). From 48 h post-treatment, a gradual decrease in mean comet parameters was noted. By 96 h of post-treatment, the mean comet tail length reached control levels indicating repair of damaged DNA. This study on mice showed different DNA damage depending on the concentration of JS-118 and the period of treatment. The present study provided further information of the potential risk of the genetic damage caused by JS-118.

Synthesis and insecticidal activity of novel carbamate derivatives as potential dual-binding site acetylcholinesterase inhibitors.

In biological systems, bivalent ligands often possess increased functional affinity for their receptors compared with monovalent ligands. On the basis of the structure of acetylcholinesterase (AChE), a series of novel carbamate heterodimetic derivatives were designed and synthesized with the aim of increasing the potency toward AChE inhibition. The AChE inhibitory ability of all the novel compounds was tested using AChE obtained from the brain of the housefly. The bioassay results showed that compounds 6j, 6k, 6m, 6n, 6p, and 6q had increased inhibitory activities in comparison with parent phenyl N-methylcarbamate (MH) at the concentration of 100 mg/L. Among them, the most potent AChE inhibitor of these compounds was 6q (IC(50) = 12 µM), which showed 62-fold greater AChE inhibitory activity than that of MH and 12-fold greater activity than metolcarb (MT), which suggested that the 3-nitrophenoxy moiety of compound 6q was able to interact with the aromatic amino acid residues lining the gorge and the phenyl N-methylcarbamate moiety was able to interact with the catalytic sites of AChE, simultaneously. The insecticidal activities of compounds 6j, 6k, 6m, 6n, 6p, and 6q were further evaluated. Consistent with the result in vitro bioassay, those compounds demonstrated better activities against Lipaphis erysimi than parent compound MH at the concentration of 300 mg/L, and compound 6q showed the best insecticidal activity, causing 98% mortality after 24 h of treatment.

Synthesis and herbicidal activity of novel N-(2,2,2)-trifluoroethylpyrazole derivatives.

A series of novel N-(2,2,2)-trifluoroethylpyrazole derivatives were synthesized, and their structures were characterized by IR, mass spectroscopy, (1)H NMR, and elementary analysis. The herbicidal activities of target compounds 10a-c and 11a-c were assessed. The bioassay results showed that these pyrazole derivatives exhibited good herbicidal activity. Compound 11a showed the best pre-emergence herbicidal effects against both dicotyledonous and monocotyledonous weeds with good safety to maize and rape at the dosage of 150 g a.i. ha(-1) in greenhouse. Field trials indicated that compound 11a exhibited better herbicidal activity by soil application than the commercial herbicide, metolachlor. Moreover, compound 11a showed the same level of safety to maize as metolachlor.