A Hybrid Approach Based on Deep CNN and Machine Learning Classifiers for the Tumor Segmentation and Classification in Brain MRI.

Conventional medical imaging and machine learning techniques are not perfect enough to correctly segment the brain tumor in MRI as the proper identification and segmentation of tumor borders are one of the most important criteria of tumor extraction. The existing approaches are time-consuming, incursive, and susceptible to human mistake. These drawbacks highlight the importance of developing a completely automated deep learning-based approach for segmentation and classification of brain tumors. The expedient and prompt segmentation and classification of a brain tumor are critical for accurate clinical diagnosis and adequately treatment. As a result, deep learning-based brain tumor segmentation and classification algorithms are extensively employed. In the deep learning-based brain tumor segmentation and classification technique, the CNN model has an excellent brain segmentation and classification effect. In this work, an integrated and hybrid approach based on deep convolutional neural network and machine learning classifiers is proposed for the accurate segmentation and classification of brain MRI tumor. A CNN is proposed in the first stage to learn the feature map from image space of brain MRI into the tumor marker region. In the second step, a faster region-based CNN is developed for the localization of tumor region followed by region proposal network (RPN). In the last step, a deep convolutional neural network and machine learning classifiers are incorporated in series in order to further refine the segmentation and classification process to obtain more accurate results and findings. The proposed model's performance is assessed based on evaluation metrics extensively used in medical image processing. The experimental results validate that the proposed deep CNN and SVM-RBF classifier achieved an accuracy of 98.3% and a dice similarity coefficient (DSC) of 97.8% on the task of classifying brain tumors as gliomas, meningioma, or pituitary using brain dataset-1, while on Figshare dataset, it achieved an accuracy of 98.0% and a DSC of 97.1% on classifying brain tumors as gliomas, meningioma, or pituitary. The segmentation and classification results demonstrate that the proposed model outperforms state-of-the-art techniques by a significant margin.

Discovery of pulmonary fibrosis inhibitor targeting TGF-β RI in Polygonum cuspidatum by high resolution mass spectrometry with in silico strategy / 药物分析学报

Pulmonary fibrosis(PF)is an irreversible lung disease that is characterized by excessive scar tissue with a poor median survival rate of 2-3 years.The inhibition of transforming growth factor-β receptor type-Ⅰ(TGF-β RI)by an appropriate drug may provide a promising strategy for the treatment of this disease.Polygonum cuspidatum(PC)is a well-known traditional Chinese herbal medicine which has an anti-PF effect.Accordingly,a combination of high resolution mass spectrometry with an in silico strategy was developed as a new method to search for potential chemical ingredients of PC that target the TGF-β RI.Based on this strategy,a total of 24 ingredients were identified.Then,absorption,distribution,meta-bolism,and excretion(ADME)-related properties were subsequently predicted to exclude compounds with potentially undesirable pharmacokinetics behaviour.Molecular docking studies on TGF-β RI were adopted to discover new PF inhibitors.Eventually,a compound that exists in PC known as resveratrol was proven to have excellent biological activity on TGF-β RI,with an ICso of 2.211 μM in vitro.Furthermore,the complex formed through molecular docking was tested via molecular dynamics simulations,which revealed that resveratrol had strong interactions with residues of TGF-β RI.This study revealed that resveratrol has significant potential as a treatment for PF due to its ability to target TGF-β RI.In addition,this research demonstrated the exploration of natural products with excellent biological activities toward specific targets via high resolution mass spectrometry in combination with in silico technology is a promising strategy for the discovery of novel drugs.

A systematic strategy for screening therapeutic constituents of (Turcz) Baill infiltrated blood-brain barrier oriented in lesions using ethanol and water extracts: a novel perspective for exploring chemical material basis of herb medicines

, a widely used Chinese herbal medicine, was considered as central nervous system (CNS) drug for years. Both ethanol extracts (EES) and water extracts (WES) of it were applied clinically. Unfortunately, the difference of their efficacy and even effective material foundation of remains obscure. In this study, to explore the active constituents of , we compared pharmacodynamics and chemical profiles / of EES/WES for the first time using multiple chemical analysis, pharmacological and data processing approaches. It was proved that there was no significant difference in the anti-depressive effects between WES and EES. However, the contents of most components and in plasma were higher in EES than those in WES, which was unconvincing for their similar efficacy. Therefore, we further explored components of targeted onto brain and the results showed that 5 lignans were identified with definite absorptivity respectively both in EES and WES caused by the limitation of blood-brain barrier. Moreover, bioinformatic analysis predicted their anti-depressive action. Above all, the systematic strategy screened 5 brain-targeted effective substances of and it was suggested that exploring the components into nidi would promote the studies on herbs effective material basis.

Quality control of Semen Ziziphi Spinosae standard decoction based on determination of multi-components using TOF-MS/MS and UPLC-PDA technology / 药物分析学报

A sensitive, fast and comprehensive method for the quality assessment of Semen Ziziphi Spinosae (SZS) standard decoction with characterization of its chemical components was developed and validated. UPLC-Q/TOF-MS/MS system was used to identify thirty-six chemical components of SZS standard de-coction which included nucleosides, phenolic acids, alkaloids, and flavonoids. Furthermore, a UPLC-PDA method was validated to simultaneously determine adenosine, protocatechuic acid, magnoflorine, ca-techin, protocatechin, vicenin II, spinosin, kaempferol-3-rutinoside, and 6'''-feruloylspinosin which re-present four species of characteristic compounds. The qualitative method had been validated according to Chinese Pharmacopoeia (2015 edition) in terms of lineary, repeatability, recovery and stability for all analytes, with the results showing good precision, accuracy and stability. In conclusion, the method using UPLC combined with MS and PDA provided a novel way for the standardization and identification of SZS standard decoction, and also offered a basis for qualitative analysis and quality assessment of the pre-parations for SZS standard decoction.