Metabolic profiling of Chimonanthus grammatus via UHPLC-HRMS-MS with computer-assisted structure elucidation and its antimicrobial activity.

Chimonanthus grammatus is used as Hakka traditional herb to treat cold, flu, etc. So far, the phytochemistry and antimicrobial compounds have not been well investigated. In this study, the orbitrap-ion trap MS was used to characterize its metabolites, combined with a computer-assisted structure elucidation method, and the antimicrobial activities were assessed by a broth dilution method against 21 human pathogens, as well as the bioassay-guided purification work to clarify its main antimicrobial compounds. A total of 83 compounds were identified with their fragmentation patterns, including terpenoids, coumarins, flavonoids, organic acids, alkaloids, and others. The plant extracts can strongly inhibit the growth of three Gram-positive and four Gram-negative bacteria, and nine active compounds were bioassay-guided isolated, including homalomenol C, jasmonic acid, isofraxidin, quercitrin, stigmasta-7,22-diene-3ß,5α,6α-triol, quercetin, 4-hydroxy-1,10-secocadin-5-ene-1,10-dione, kaempferol, and E-4-(4,8-dimethylnona-3,7-dienyl)furan-2(5H)-one. Among them, isofraxidin, kaempferol, and quercitrin showed significant activity against planktonic Staphylococcus aureus (IC50 = 13.51, 18.08 and 15.86 µg/ml). Moreover, their antibiofilm activities of S. aureus (BIC50 = 15.43, 17.31, 18.86 µg/ml; BEC50 = 45.86, ≥62.50, and 57.62 µg/ml) are higher than ciprofloxacin. The results demonstrated that the isolated antimicrobial compounds played the key role of this herb in combating microbes and provided benefits for its development and quality control, and the computer-assisted structure elucidation method was a powerful tool for chemical analysis, especially for distinguishing isomers with similar structures, which can be used for other complex samples.

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features.

Epilepsy is a brain disorder disease that affects people's quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5-40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN-RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN-RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN-RNN classification procedure. The results revealed that the proposed CNN-RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.

A deep convolutional neural network for segmentation of whole-slide pathology images identifies novel tumour cell-perivascular niche interactions that are associated with poor survival in glioblastoma.

BACKGROUND: Glioblastoma is the most aggressive type of brain cancer with high-levels of intra- and inter-tumour heterogeneity that contribute to its rapid growth and invasion within the brain. However, a spatial characterisation of gene signatures and the cell types expressing these in different tumour locations is still lacking. METHODS: We have used a deep convolutional neural network (DCNN) as a semantic segmentation model to segment seven different tumour regions including leading edge (LE), infiltrating tumour (IT), cellular tumour (CT), cellular tumour microvascular proliferation (CTmvp), cellular tumour pseudopalisading region around necrosis (CTpan), cellular tumour perinecrotic zones (CTpnz) and cellular tumour necrosis (CTne) in digitised glioblastoma histopathological slides from The Cancer Genome Atlas (TCGA). Correlation analysis between segmentation results from tumour images together with matched RNA expression data was performed to identify genetic signatures that are specific to different tumour regions. RESULTS: We found that spatially resolved gene signatures were strongly correlated with survival in patients with defined genetic mutations. Further in silico cell ontology analysis along with single-cell RNA sequencing data from resected glioblastoma tissue samples showed that these tumour regions had different gene signatures, whose expression was driven by different cell types in the regional tumour microenvironment. Our results further pointed to a key role for interactions between microglia/pericytes/monocytes and tumour cells that occur in the IT and CTmvp regions, which may contribute to poor patient survival. CONCLUSIONS: This work identified key histopathological features that correlate with patient survival and detected spatially associated genetic signatures that contribute to tumour-stroma interactions and which should be investigated as new targets in glioblastoma. The source codes and datasets used are available in GitHub: https://github.com/amin20/GBM_WSSM .

The Application of Deep Convolutional Neural Networks to Brain Cancer Images: A Survey.

In recent years, improved deep learning techniques have been applied to biomedical image processing for the classification and segmentation of different tumors based on magnetic resonance imaging (MRI) and histopathological imaging (H&E) clinical information. Deep Convolutional Neural Networks (DCNNs) architectures include tens to hundreds of processing layers that can extract multiple levels of features in image-based data, which would be otherwise very difficult and time-consuming to be recognized and extracted by experts for classification of tumors into different tumor types, as well as segmentation of tumor images. This article summarizes the latest studies of deep learning techniques applied to three different kinds of brain cancer medical images (histology, magnetic resonance, and computed tomography) and highlights current challenges in the field for the broader applicability of DCNN in personalized brain cancer care by focusing on two main applications of DCNNs: classification and segmentation of brain cancer tumors images.

Process optimisation for green synthesis of zero-valent iron nanoparticles using Mentha piperita.

The potential of Mentha piperita in the iron nanoparticles (FeNPs) production was evaluated for the first time. The influences of the variables such as incubation time, temperature, and volume ratio of the extract to metal ions on the nanoparticle size were investigated using central composite design. The appearance of SPR bands at 284 nm in UV-Vis spectra of the mixtures verified the nanoparticle formation. Incubating the aqueous extract and metal precursor with 1.5 volume ratio at 50°C for 30 min leads to the formation of the smallest nanoparticles with the narrowest size distribution. At the optimal condition, the nanoparticles were found to be within the range of 35-50 nm. Experimental measurements of the average nanoparticle size were fitted well to the polynomial model satisfactory with R2 of 0.9078. Among all model terms, the linear term of temperature, the quadratic terms of temperature, and mixing volume ratio have the significant effects on the nanoparticle average size. FeNPs produced at the optimal condition were characterised by transmission electron microscopy, thermogravimetry analysis (TGA), and Fourier-transform infrared spectroscopy. The observed weight loss in the TGA curve confirms the encapsulation of FeNPs by the biomolecules of the extract which were dissociated by heat.

Predicting protein structural classes based on complex networks and recurrence analysis.

Protein sequences are divided into four structural classes. The determination of class is a challenging and beneficial task in the bioinformatics field. Several methods have been proposed to this end, but most utilize too many features and produce unsuitable results. In the present, features are extracted based on the predicted secondary structures. At first, predicted secondary structure sequences are mapped into two time series by the chaos game representation. Then, a recurrence matrix is calculated from each of the time series. The recurrence matrix is identified with the adjacency matrix of a complex network and measures are applied for the characterization of complex networks to these recurrence matrixes. For a given protein sequence, a total of 24 characteristic features can be calculated and these are fed into Fisher's discriminated analysis algorithm for classification. To examine the proposed method, two widely used low similarity benchmark datasets design and test its performance. A comparison with the results of existing methods shows that the current study's approach provides a satisfactory performance for protein structural class prediction.